Gueule-d-ange/llama32-3b-redo-dpo_simple
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: Gueule-d-ange/llama32-3b-redo-dpo_simple
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-08-29 12:08:17 +08:00
commit 51c9b49e18
28 changed files with 4958 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

36
.gitattributes vendored Normal file
View File

@@ -0,0 +1,36 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
tokenizer.json filter=lfs diff=lfs merge=lfs -text

71
README.md Normal file
View File

@@ -0,0 +1,71 @@
---
base_model: meta-llama/Llama-3.2-3B
library_name: transformers
model_name: llama32-3b-redo-dpo_simple
tags:
- generated_from_trainer
- dpo
- llama-factory
- full
- trl
licence: license
---
# Model Card for llama32-3b-redo-dpo_simple
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Gueule-d-ange/llama32-3b-redo-dpo_simple", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.24.0
- Transformers: 4.57.1
- Pytorch: 2.12.1
- Datasets: 4.0.0
- Tokenizers: 0.22.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```

26
all_results.json Normal file
View File

@@ -0,0 +1,26 @@
{
"epoch": 1.0,
"eval_logits/chosen": -1.3287253379821777,
"eval_logits/rejected": -1.3337767124176025,
"eval_logps/chosen": -316.3905944824219,
"eval_logps/rejected": -254.8103485107422,
"eval_loss": 0.4671759009361267,
"eval_metrics/advantage_var": 125.51482391357422,
"eval_rewards/accuracies": 0.7835859060287476,
"eval_rewards/chosen": 1.0432024002075195,
"eval_rewards/margins": 1.0373461246490479,
"eval_rewards/rejected": 0.005856274161487818,
"eval_runtime": 273.8577,
"eval_samples_per_second": 14.438,
"eval_stability/repetition_rate_mean": 0.8705078363418579,
"eval_stability/response_length_mean": 512.0,
"eval_stability/response_length_std": 0.0,
"eval_stability/response_length_var": 0.0,
"eval_stability/token_entropy_mean": 1.601953148841858,
"eval_steps_per_second": 1.808,
"total_flos": 1.761935585720664e+18,
"train_loss": 0.5203882457111775,
"train_runtime": 14354.8151,
"train_samples_per_second": 4.13,
"train_steps_per_second": 0.065
}

7
chat_template.jinja Normal file
View File

@@ -0,0 +1,7 @@
{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>
' + system_message + '<|eot_id|>' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>
' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>
' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}

36
config.json Normal file
View File

@@ -0,0 +1,36 @@
{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": 128009,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 24,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 128009,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": true,
"transformers_version": "4.57.1",
"use_cache": false,
"vocab_size": 128256
}

21
eval_results.json Normal file
View File

@@ -0,0 +1,21 @@
{
"epoch": 1.0,
"eval_logits/chosen": -1.3287253379821777,
"eval_logits/rejected": -1.3337767124176025,
"eval_logps/chosen": -316.3905944824219,
"eval_logps/rejected": -254.8103485107422,
"eval_loss": 0.4671759009361267,
"eval_metrics/advantage_var": 125.51482391357422,
"eval_rewards/accuracies": 0.7835859060287476,
"eval_rewards/chosen": 1.0432024002075195,
"eval_rewards/margins": 1.0373461246490479,
"eval_rewards/rejected": 0.005856274161487818,
"eval_runtime": 273.8577,
"eval_samples_per_second": 14.438,
"eval_stability/repetition_rate_mean": 0.8705078363418579,
"eval_stability/response_length_mean": 512.0,
"eval_stability/response_length_std": 0.0,
"eval_stability/response_length_var": 0.0,
"eval_stability/token_entropy_mean": 1.601953148841858,
"eval_steps_per_second": 1.808
}

13
generation_config.json Normal file
View File

@@ -0,0 +1,13 @@
{
"_from_model_config": true,
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128009,
128001
],
"pad_token_id": 128009,
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "4.57.1"
}

3
model-00001-of-00002.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:809afab775d6ee88e70155e4d52717e3778221670b4900475dc68d799c45a715
size 4965799096

3
model-00002-of-00002.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b91f90461124b47191505b2837b9f271a0e66cf76322082a631a393d797989f9
size 2247734992

263
model.safetensors.index.json Normal file
View File

@@ -0,0 +1,263 @@
{
"metadata": {
"total_parameters": 3212749824,
"total_size": 7213504512
},
"weight_map": {
"lm_head.weight": "model-00002-of-00002.safetensors",
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.20.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.20.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.20.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.21.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.21.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.21.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.22.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.22.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.22.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.23.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.23.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.23.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.24.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.24.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.24.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.25.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.25.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.25.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.26.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.26.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.26.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.27.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.27.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.27.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.norm.weight": "model-00002-of-00002.safetensors"
}
}

130
qualitative_epoch_1.csv Normal file
View File

@@ -0,0 +1,130 @@
epoch,global_step,prompt_id,prompt,response
1,927,0,"If someone naively says they will ""pick a random natural number uniformly,"" the probability of any fixed number seems to be zero, yet something gets picked. Walk a reader through what breaks in the usual probability story here, and mention at least two different ways mathematicians repair the intuition (measure theory, finitely additive approaches, ""don't ask that question,"" etc.).","Try to give the flavor of the measure theory approach.2013-10-12_03-47-21
Let's say we have a sequence of natural numbers, $(a_n)_{n=1}^\infty$. A sequence of natural numbers is just a function $a: \mathbb{N} \to \mathbb{N}$.
If $n \in \mathbb{N}$, $a(n)$ is the $n$th natural number.
For example, if $a(n) = n^2$, then $a(1) = 1^2 = 1$, $a(2) = 2^2 = 4$, $a(3) = 3^2 = 9$, etc.
We will define a probability measure on the set of natural numbers by saying that the probability of $a(n)$ being chosen is $\frac{1}{n}$.
What's the probability of $a(n)$ being chosen if $n$ is odd? The probability of $a(n)$ being chosen if $n$ is even?
The probability of $a(n)$ being chosen is $\frac{1}{n}$, regardless of whether $n$ is odd or even. To see this, consider the probability of $a(n)$ being chosen if $n$ is even.
Let $E$ be the event that $n$ is even. Then $E$ is an event and has probability $\frac{1}{2}$. Let $A$ be the set of natural numbers that are even. Then
\begin{align*} P(a(n) = a(2k)) &= P(a(n) \in A)\\ &= P(a(n) \in A | E) P(E) + P(a(n) \in A | \overline{E}) P(\overline{E})\\ &= \frac{1}{2} \cdot \frac{1}{2} + 0 \cdot \frac{1}{2}\\ &= \frac{1}{4}. \end{align*}
The probability of $a(n)$ being chosen if $n$ is odd is exactly the same, so the probability of $a(n)$ being chosen is $\frac{1}{4}$ if $n$ is even and $\frac{3}{4}$ if $n$ is odd.
The probability of $a(n)$ being chosen is $\frac{1}{n}$. The probability of $"
1,927,1,"Explain why GPUs are not just ""faster CPUs"" for training neural networks—discuss memory bandwidth, batching, kernel fusion, and communication, at a level where someone could draw a wrong cartoon and a better cartoon of what is happening.","500 words, no citations, no references, no notes, no quotes, no diagrams, no images, no code. I want a coherent, simple, and readable explanation.What is a GPU? GPUs are graphics processing units, which are a type of coprocessor used for performing calculations in parallel, rather than sequentially, as a CPU does. They are typically used for rendering graphics and video, and more recently, they have been used for machine learning and deep learning. GPUs are optimized for parallel processing, making them particularly well-suited for tasks that require many computations, such as neural network training, image processing, and data analysis. GPUs typically have many cores, which can work in parallel, and are designed to perform mathematical operations like matrix multiplication, which are essential for neural network training. GPUs have a high bandwidth and fast memory access, which enables them to handle large datasets efficiently. GPUs also have a specialized architecture that makes them well-suited for parallel processing, allowing them to perform multiple computations at once. GPUs are not just ""faster CPUs"" for training neural networks, but they are a specialized hardware designed for parallel processing and have advantages over CPUs in terms of memory bandwidth, batching, kernel fusion, and communication. GPU vs. CPU for Neural Network Training: Memory Bandwidth: GPUs have a much higher bandwidth compared to CPUs, which means they can process data faster and handle large datasets more efficiently. CPUs, on the other hand, have a limited bandwidth, which can become a bottleneck when working with large datasets. Batching: GPUs can work with larger batches of data, which can significantly speed up the training process. CPUs, on the other hand, have a limit on the size of batches they can work with, making GPUs more efficient in terms of training time. Kernel Fusion: GPUs have a specialized architecture that allows for kernel fusion, which means that multiple instructions can be fused into a single operation, resulting in faster execution. CPUs, on the other hand, are not as efficient in terms of kernel fusion, making GPUs more efficient in terms of performance. Communication: GPUs have a specialized architecture that makes them more efficient in terms of communication, allowing them to communicate with each other more efficiently. CPUs, on the other hand, have a limited communication capacity, which can become a bottleneck when working with large datasets. GPUs are not just ""faster CPUs"" for training neural networks, but they have specialized hardware and architecture that make them more efficient in terms of memory bandwidth, batching, kernel fusion, and communication, which allows them"
1,927,2,"Some philosophers defend views that feel almost like performance art. Explain—as if to a curious undergrad—three different interpretations of what serious philosophers might be doing when they advocate bizarre-sounding positions (truth-seeking, steelmanning, career incentives, dialectical usefulness, etc.).","How would you respond to someone who thinks that these views are either uninteresting or incoherent?
What is a serious philosopher?
A serious philosopher is one who is not afraid to express a bold, controversial, or radical view. A serious philosopher is one who is not afraid to be wrong. A serious philosopher is one who is not afraid to be thought of as foolish, or eccentric, or even crazy. A serious philosopher is one who is not afraid to be ridiculed, or reviled, or reviled and ridiculed.
What is a serious philosopher’s job?
The job of a serious philosopher is to find the truth. The job of a serious philosopher is to find the truth about reality. The job of a serious philosopher is to find the truth about the universe. The job of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s work?
The work of a serious philosopher is to find the truth. The work of a serious philosopher is to find the truth about reality. The work of a serious philosopher is to find the truth about the universe. The work of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s aim?
The aim of a serious philosopher is to find the truth. The aim of a serious philosopher is to find the truth about reality. The aim of a serious philosopher is to find the truth about the universe. The aim of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s goal?
The goal of a serious philosopher is to find the truth. The goal of a serious philosopher is to find the truth about reality. The goal of a serious philosopher is to find the truth about the universe. The goal of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s task?
The task of a serious philosopher is to find the truth. The task of a serious philosopher is to find the truth about reality. The task of a serious philosopher is to find the truth about the universe. The task of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s mission?
The mission of a serious philosopher is to find the truth. The mission of a serious philosopher is to find the truth about reality. The mission of a serious philosopher is to find the truth about the universe. The mission of a serious philosopher is to find the truth about the world.
What is a serious philosopher’s role?
The role of a serious philosopher is to find the truth. The"
1,927,3,"You are first author on a messy multi-lab paper; deadlines slip and people ghost you until the last minute but rarely say ""no."" Outline a delegation and communication system that is kind, repeatability-based, and escalates without humiliating juniors.","This is a very common issue. When I was a postdoc, I was the first author on a 10-author paper. It was a mess. People were ghosting me and I was ghosting people. It was not a great experience.
I'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this? I'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this?
As a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper? As a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper?
How can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be. How can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be.
I've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this? I've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this?
I have a new postdoc, and I need to write a new lab manual. How do I"
1,927,4,"You suddenly need to teach a semester of project-first high school ""engineering"" to beginners, with a shoestring budget. Propose a progression of hands-on milestones (weeks 1–16 mindset, not a shopping list) that could end in something ambitious without setting everyone up for only frustration.","What would you do, and what would you keep in mind? And how would you scale the same approach to teaching a full semester of ""engineering"" at a high school?
We're in a situation where we have a few weeks to put together a semester-long project-first class at a high school, and I'm asking for a bit of guidance. The project will be a high-altitude weather balloon launch, with a payload that could include a GPS, a temperature sensor, a barometer, a camera, and a small LED (or perhaps a few LEDs). The students will build and launch the balloon, and then analyze the data they collect. We're not building a rocket, but we're also not doing a simple balloon launch with a sensor attached.
What we have:
A school with a few dozen high school students (ages 14–18) who have had little to no engineering education, but who are interested in engineering and are motivated by the prospect of building something, launching it, and analyzing the data.
A few hundred dollars for supplies (some of which will come from the school, but not all).
A 3D printer that's not particularly fast, but will be adequate for the first version of the balloon.
A few hundred feet of PVC pipe (and associated fittings) that will be used to build the balloon.
A 6V battery (perhaps 1000 mAh) that will be used to power the payload.
A few hundred feet of 1/4"" copper wire that will be used for the payload's antenna.
The students will be doing this on their own time, in their free time, and in their spare time.
A few weeks to build and launch the balloon.
A semester-long class (or a semester of classes, depending on how we structure the class).
A few hundred feet of 1/4"" copper wire that will be used for the payload's antenna.
A 6V battery (perhaps 1000 mAh) that will be used to power the payload.
A few hundred feet of 1/4"" copper wire that will be used for the payload's antenna.
A 6V battery (perhaps 1000 mAh) that will be used to power the payload.
A few hundred feet of 1/4"" copper wire that will be used for the payload's antenna.
A 6V battery (perhaps 1000 mAh) that will be used to power the payload.
A few hundred feet of 1/4"" copper wire that will be used"
1,927,5,Write a 250-word story where the twist hinges on a misunderstanding of conditional probability.,"You may use a standard conditional probability formula, but you must show the reader how to understand the relationship.
Example: Two brothers, Jack and Jill, are trying to decide which of them will marry the woman they both like. If Jill marries the woman, then Jack will not, and vice versa. If Jack marries the woman, then Jill will not. The brothers know that the probability that Jill will marry the woman is 0.7, and that the probability that Jack will marry the woman is 0.4. Which brother will marry the woman?
Solution: We can represent this scenario using the following Venn diagram:
The probability that Jill will marry the woman is 0.7, so the shaded region is 0.7. The probability that Jack will marry the woman is 0.4, so the shaded region is 0.4. The probability that Jill will marry the woman is 0.7, and the probability that Jack will marry the woman is 0.4, so the probability that neither brother will marry the woman is 1 - 0.7 - 0.4 = 0.9. This means that the probability that one brother will marry the woman is 0.1, so the probability that Jack will marry the woman is 0.1. Therefore, Jack will marry the woman.
In your story, you should describe a scenario where the twist hinges on a misunderstanding of conditional probability. You should show the reader how to understand the relationship between the conditional probability and the probability of the event in question.
The story should be written in 250 words or less, and it should be in the form of a short story or a poem. You should include a title and a brief description of the story. You should also include a reference to the conditional probability formula and how it was used to solve the problem.
For example, here's a story that uses a misunderstanding of conditional probability as the twist:
A man named John was trying to decide whether to take a job in a new city. He had two options: Option 1: Take the job and move to the new city, or Option 2: Stay in his current city and continue working there. He knew that the probability of getting a job in the new city was 0.8, and the probability of getting a job in his current city was 0.2. He also knew that if he took the job in the new city, then he would have to move to the new city, and if"
1,927,6,"Engineering students debate whether chasing all-A grades is worth it versus projects and sleep. Analyze what grades do and do not proxy for, first-job signaling, learning depth, and opportunity cost—end with conditional advice for different student goals.","(10 minutes)
Grading is an important, but often misunderstood, part of your education. In this episode, we discuss the differences between high school, college, and grad school grading systems, the importance of grading feedback, and how to optimize your grades in each system. (20 minutes)
In this episode, we explore the different types of grades that universities use, including cumulative GPA, class grades, and pass/fail. We discuss the importance of understanding the grading system at your university and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, and weighted GPA. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the importance of understanding the grading system at your university, including the difference between cumulative GPA and class grades, the impact of weighted and unweighted GPAs, and how to manage your grade point average. (20 minutes)
In this episode, we explore the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also discuss the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)
In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore"
1,927,7,"You are a 10x engineer in a startup with a legacy monolith. You want to build a new system, but the existing code is a spaghetti monster. How do you start? Outline a plan to refactor the codebase without rewriting everything.","You will need to think about the following:
- How do you extract a single class out of the codebase?
- How do you test your code?
- How do you keep the existing functionality intact while you refactor?
- How do you refactor the code in the most maintainable way possible?
- How do you communicate with your team about the refactor process?
In this kata, you will write a function that refactors a given JavaScript code snippet to make it more maintainable and testable. You will need to use the following techniques:
- Refactoring
- Extracting classes
- Extracting methods
- Extracting functions
- Refactoring with tests
- Communicating with your team
- Working in a team
This kata is a practical application of the principles of test-driven development and code refactoring. You will need to apply these principles to a real-world scenario, which will help you to understand how to apply them in practice.
You will also need to use the following tools:
- A code editor (such as VS Code, Sublime Text, or Atom)
- A version control system (such as Git or Bitbucket)
- A testing framework (such as Mocha, Jest, or Jest)
This kata will take you about 2-3 hours to complete, depending on your experience and the complexity of the code snippet you start with. You can use the following resources to help you:
- [Refactoring](https://en.wikipedia.org/wiki/Code_refactoring)
- [Test-Driven Development](https://en.wikipedia.org/wiki/Test-driven_development)
- [Extracting classes](https://en.wikipedia.org/wiki/Extract_class)
- [Extracting methods](https://en.wikipedia.org/wiki/Extract_method)
- [Extracting functions](https://en.wikipedia.org/wiki/Extract_function)
- [Refactoring with tests](https://en.wikipedia.org/wiki/Test-driven_development#Refactoring)
- [Communicating with your team](https://en.wikipedia.org/wiki/Communication)
- [Working in a team](https://en.wikipedia.org/wiki/Teamwork)
Good luck and have fun!
Isolate the classes in the given code snippet:
```javascript
const app = {
state: {
user: {
name: 'John',
age: 30
},
posts: [
{
id: 1,
title: 'My first post',
content: 'This is my first post.'
},
{
id: 2,
title: '"
1 epoch global_step prompt_id prompt response
2 1 927 0 If someone naively says they will "pick a random natural number uniformly," the probability of any fixed number seems to be zero, yet something gets picked. Walk a reader through what breaks in the usual probability story here, and mention at least two different ways mathematicians repair the intuition (measure theory, finitely additive approaches, "don't ask that question," etc.). Try to give the flavor of the measure theory approach.2013-10-12_03-47-21 Let's say we have a sequence of natural numbers, $(a_n)_{n=1}^\infty$. A sequence of natural numbers is just a function $a: \mathbb{N} \to \mathbb{N}$. If $n \in \mathbb{N}$, $a(n)$ is the $n$th natural number. For example, if $a(n) = n^2$, then $a(1) = 1^2 = 1$, $a(2) = 2^2 = 4$, $a(3) = 3^2 = 9$, etc. We will define a probability measure on the set of natural numbers by saying that the probability of $a(n)$ being chosen is $\frac{1}{n}$. What's the probability of $a(n)$ being chosen if $n$ is odd? The probability of $a(n)$ being chosen if $n$ is even? The probability of $a(n)$ being chosen is $\frac{1}{n}$, regardless of whether $n$ is odd or even. To see this, consider the probability of $a(n)$ being chosen if $n$ is even. Let $E$ be the event that $n$ is even. Then $E$ is an event and has probability $\frac{1}{2}$. Let $A$ be the set of natural numbers that are even. Then \begin{align*} P(a(n) = a(2k)) &= P(a(n) \in A)\\ &= P(a(n) \in A | E) P(E) + P(a(n) \in A | \overline{E}) P(\overline{E})\\ &= \frac{1}{2} \cdot \frac{1}{2} + 0 \cdot \frac{1}{2}\\ &= \frac{1}{4}. \end{align*} The probability of $a(n)$ being chosen if $n$ is odd is exactly the same, so the probability of $a(n)$ being chosen is $\frac{1}{4}$ if $n$ is even and $\frac{3}{4}$ if $n$ is odd. The probability of $a(n)$ being chosen is $\frac{1}{n}$. The probability of $
3 1 927 1 Explain why GPUs are not just "faster CPUs" for training neural networks—discuss memory bandwidth, batching, kernel fusion, and communication, at a level where someone could draw a wrong cartoon and a better cartoon of what is happening. 500 words, no citations, no references, no notes, no quotes, no diagrams, no images, no code. I want a coherent, simple, and readable explanation.What is a GPU? GPUs are graphics processing units, which are a type of coprocessor used for performing calculations in parallel, rather than sequentially, as a CPU does. They are typically used for rendering graphics and video, and more recently, they have been used for machine learning and deep learning. GPUs are optimized for parallel processing, making them particularly well-suited for tasks that require many computations, such as neural network training, image processing, and data analysis. GPUs typically have many cores, which can work in parallel, and are designed to perform mathematical operations like matrix multiplication, which are essential for neural network training. GPUs have a high bandwidth and fast memory access, which enables them to handle large datasets efficiently. GPUs also have a specialized architecture that makes them well-suited for parallel processing, allowing them to perform multiple computations at once. GPUs are not just "faster CPUs" for training neural networks, but they are a specialized hardware designed for parallel processing and have advantages over CPUs in terms of memory bandwidth, batching, kernel fusion, and communication. GPU vs. CPU for Neural Network Training: Memory Bandwidth: GPUs have a much higher bandwidth compared to CPUs, which means they can process data faster and handle large datasets more efficiently. CPUs, on the other hand, have a limited bandwidth, which can become a bottleneck when working with large datasets. Batching: GPUs can work with larger batches of data, which can significantly speed up the training process. CPUs, on the other hand, have a limit on the size of batches they can work with, making GPUs more efficient in terms of training time. Kernel Fusion: GPUs have a specialized architecture that allows for kernel fusion, which means that multiple instructions can be fused into a single operation, resulting in faster execution. CPUs, on the other hand, are not as efficient in terms of kernel fusion, making GPUs more efficient in terms of performance. Communication: GPUs have a specialized architecture that makes them more efficient in terms of communication, allowing them to communicate with each other more efficiently. CPUs, on the other hand, have a limited communication capacity, which can become a bottleneck when working with large datasets. GPUs are not just "faster CPUs" for training neural networks, but they have specialized hardware and architecture that make them more efficient in terms of memory bandwidth, batching, kernel fusion, and communication, which allows them
4 1 927 2 Some philosophers defend views that feel almost like performance art. Explain—as if to a curious undergrad—three different interpretations of what serious philosophers might be doing when they advocate bizarre-sounding positions (truth-seeking, steelmanning, career incentives, dialectical usefulness, etc.). How would you respond to someone who thinks that these views are either uninteresting or incoherent? What is a serious philosopher? A serious philosopher is one who is not afraid to express a bold, controversial, or radical view. A serious philosopher is one who is not afraid to be wrong. A serious philosopher is one who is not afraid to be thought of as foolish, or eccentric, or even crazy. A serious philosopher is one who is not afraid to be ridiculed, or reviled, or reviled and ridiculed. What is a serious philosopher’s job? The job of a serious philosopher is to find the truth. The job of a serious philosopher is to find the truth about reality. The job of a serious philosopher is to find the truth about the universe. The job of a serious philosopher is to find the truth about the world. What is a serious philosopher’s work? The work of a serious philosopher is to find the truth. The work of a serious philosopher is to find the truth about reality. The work of a serious philosopher is to find the truth about the universe. The work of a serious philosopher is to find the truth about the world. What is a serious philosopher’s aim? The aim of a serious philosopher is to find the truth. The aim of a serious philosopher is to find the truth about reality. The aim of a serious philosopher is to find the truth about the universe. The aim of a serious philosopher is to find the truth about the world. What is a serious philosopher’s goal? The goal of a serious philosopher is to find the truth. The goal of a serious philosopher is to find the truth about reality. The goal of a serious philosopher is to find the truth about the universe. The goal of a serious philosopher is to find the truth about the world. What is a serious philosopher’s task? The task of a serious philosopher is to find the truth. The task of a serious philosopher is to find the truth about reality. The task of a serious philosopher is to find the truth about the universe. The task of a serious philosopher is to find the truth about the world. What is a serious philosopher’s mission? The mission of a serious philosopher is to find the truth. The mission of a serious philosopher is to find the truth about reality. The mission of a serious philosopher is to find the truth about the universe. The mission of a serious philosopher is to find the truth about the world. What is a serious philosopher’s role? The role of a serious philosopher is to find the truth. The
5 1 927 3 You are first author on a messy multi-lab paper; deadlines slip and people ghost you until the last minute but rarely say "no." Outline a delegation and communication system that is kind, repeatability-based, and escalates without humiliating juniors. This is a very common issue. When I was a postdoc, I was the first author on a 10-author paper. It was a mess. People were ghosting me and I was ghosting people. It was not a great experience. I'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this? I'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this? As a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper? As a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper? How can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be. How can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be. I've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this? I've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this? I have a new postdoc, and I need to write a new lab manual. How do I
6 1 927 4 You suddenly need to teach a semester of project-first high school "engineering" to beginners, with a shoestring budget. Propose a progression of hands-on milestones (weeks 1–16 mindset, not a shopping list) that could end in something ambitious without setting everyone up for only frustration. What would you do, and what would you keep in mind? And how would you scale the same approach to teaching a full semester of "engineering" at a high school? We're in a situation where we have a few weeks to put together a semester-long project-first class at a high school, and I'm asking for a bit of guidance. The project will be a high-altitude weather balloon launch, with a payload that could include a GPS, a temperature sensor, a barometer, a camera, and a small LED (or perhaps a few LEDs). The students will build and launch the balloon, and then analyze the data they collect. We're not building a rocket, but we're also not doing a simple balloon launch with a sensor attached. What we have: A school with a few dozen high school students (ages 14–18) who have had little to no engineering education, but who are interested in engineering and are motivated by the prospect of building something, launching it, and analyzing the data. A few hundred dollars for supplies (some of which will come from the school, but not all). A 3D printer that's not particularly fast, but will be adequate for the first version of the balloon. A few hundred feet of PVC pipe (and associated fittings) that will be used to build the balloon. A 6V battery (perhaps 1000 mAh) that will be used to power the payload. A few hundred feet of 1/4" copper wire that will be used for the payload's antenna. The students will be doing this on their own time, in their free time, and in their spare time. A few weeks to build and launch the balloon. A semester-long class (or a semester of classes, depending on how we structure the class). A few hundred feet of 1/4" copper wire that will be used for the payload's antenna. A 6V battery (perhaps 1000 mAh) that will be used to power the payload. A few hundred feet of 1/4" copper wire that will be used for the payload's antenna. A 6V battery (perhaps 1000 mAh) that will be used to power the payload. A few hundred feet of 1/4" copper wire that will be used for the payload's antenna. A 6V battery (perhaps 1000 mAh) that will be used to power the payload. A few hundred feet of 1/4" copper wire that will be used
7 1 927 5 Write a 250-word story where the twist hinges on a misunderstanding of conditional probability. You may use a standard conditional probability formula, but you must show the reader how to understand the relationship. Example: Two brothers, Jack and Jill, are trying to decide which of them will marry the woman they both like. If Jill marries the woman, then Jack will not, and vice versa. If Jack marries the woman, then Jill will not. The brothers know that the probability that Jill will marry the woman is 0.7, and that the probability that Jack will marry the woman is 0.4. Which brother will marry the woman? Solution: We can represent this scenario using the following Venn diagram: The probability that Jill will marry the woman is 0.7, so the shaded region is 0.7. The probability that Jack will marry the woman is 0.4, so the shaded region is 0.4. The probability that Jill will marry the woman is 0.7, and the probability that Jack will marry the woman is 0.4, so the probability that neither brother will marry the woman is 1 - 0.7 - 0.4 = 0.9. This means that the probability that one brother will marry the woman is 0.1, so the probability that Jack will marry the woman is 0.1. Therefore, Jack will marry the woman. In your story, you should describe a scenario where the twist hinges on a misunderstanding of conditional probability. You should show the reader how to understand the relationship between the conditional probability and the probability of the event in question. The story should be written in 250 words or less, and it should be in the form of a short story or a poem. You should include a title and a brief description of the story. You should also include a reference to the conditional probability formula and how it was used to solve the problem. For example, here's a story that uses a misunderstanding of conditional probability as the twist: A man named John was trying to decide whether to take a job in a new city. He had two options: Option 1: Take the job and move to the new city, or Option 2: Stay in his current city and continue working there. He knew that the probability of getting a job in the new city was 0.8, and the probability of getting a job in his current city was 0.2. He also knew that if he took the job in the new city, then he would have to move to the new city, and if
8 1 927 6 Engineering students debate whether chasing all-A grades is worth it versus projects and sleep. Analyze what grades do and do not proxy for, first-job signaling, learning depth, and opportunity cost—end with conditional advice for different student goals. (10 minutes) Grading is an important, but often misunderstood, part of your education. In this episode, we discuss the differences between high school, college, and grad school grading systems, the importance of grading feedback, and how to optimize your grades in each system. (20 minutes) In this episode, we explore the different types of grades that universities use, including cumulative GPA, class grades, and pass/fail. We discuss the importance of understanding the grading system at your university and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, and weighted GPA. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the importance of understanding the grading system at your university, including the difference between cumulative GPA and class grades, the impact of weighted and unweighted GPAs, and how to manage your grade point average. (20 minutes) In this episode, we explore the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also discuss the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes) In this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore
9 1 927 7 You are a 10x engineer in a startup with a legacy monolith. You want to build a new system, but the existing code is a spaghetti monster. How do you start? Outline a plan to refactor the codebase without rewriting everything. You will need to think about the following: - How do you extract a single class out of the codebase? - How do you test your code? - How do you keep the existing functionality intact while you refactor? - How do you refactor the code in the most maintainable way possible? - How do you communicate with your team about the refactor process? In this kata, you will write a function that refactors a given JavaScript code snippet to make it more maintainable and testable. You will need to use the following techniques: - Refactoring - Extracting classes - Extracting methods - Extracting functions - Refactoring with tests - Communicating with your team - Working in a team This kata is a practical application of the principles of test-driven development and code refactoring. You will need to apply these principles to a real-world scenario, which will help you to understand how to apply them in practice. You will also need to use the following tools: - A code editor (such as VS Code, Sublime Text, or Atom) - A version control system (such as Git or Bitbucket) - A testing framework (such as Mocha, Jest, or Jest) This kata will take you about 2-3 hours to complete, depending on your experience and the complexity of the code snippet you start with. You can use the following resources to help you: - [Refactoring](https://en.wikipedia.org/wiki/Code_refactoring) - [Test-Driven Development](https://en.wikipedia.org/wiki/Test-driven_development) - [Extracting classes](https://en.wikipedia.org/wiki/Extract_class) - [Extracting methods](https://en.wikipedia.org/wiki/Extract_method) - [Extracting functions](https://en.wikipedia.org/wiki/Extract_function) - [Refactoring with tests](https://en.wikipedia.org/wiki/Test-driven_development#Refactoring) - [Communicating with your team](https://en.wikipedia.org/wiki/Communication) - [Working in a team](https://en.wikipedia.org/wiki/Teamwork) Good luck and have fun! Isolate the classes in the given code snippet: ```javascript const app = { state: { user: { name: 'John', age: 30 }, posts: [ { id: 1, title: 'My first post', content: 'This is my first post.' }, { id: 2, title: '

8
qualitative_epoch_1.jsonl Normal file
View File

@@ -0,0 +1,8 @@
{"epoch": 1, "global_step": 927, "prompt_id": 0, "prompt": "If someone naively says they will \"pick a random natural number uniformly,\" the probability of any fixed number seems to be zero, yet something gets picked. Walk a reader through what breaks in the usual probability story here, and mention at least two different ways mathematicians repair the intuition (measure theory, finitely additive approaches, \"don't ask that question,\" etc.).", "response": "Try to give the flavor of the measure theory approach.2013-10-12_03-47-21\nLet's say we have a sequence of natural numbers, $(a_n)_{n=1}^\\infty$. A sequence of natural numbers is just a function $a: \\mathbb{N} \\to \\mathbb{N}$.\nIf $n \\in \\mathbb{N}$, $a(n)$ is the $n$th natural number.\nFor example, if $a(n) = n^2$, then $a(1) = 1^2 = 1$, $a(2) = 2^2 = 4$, $a(3) = 3^2 = 9$, etc.\nWe will define a probability measure on the set of natural numbers by saying that the probability of $a(n)$ being chosen is $\\frac{1}{n}$.\nWhat's the probability of $a(n)$ being chosen if $n$ is odd? The probability of $a(n)$ being chosen if $n$ is even?\nThe probability of $a(n)$ being chosen is $\\frac{1}{n}$, regardless of whether $n$ is odd or even. To see this, consider the probability of $a(n)$ being chosen if $n$ is even.\nLet $E$ be the event that $n$ is even. Then $E$ is an event and has probability $\\frac{1}{2}$. Let $A$ be the set of natural numbers that are even. Then\n\\begin{align*} P(a(n) = a(2k)) &= P(a(n) \\in A)\\\\ &= P(a(n) \\in A | E) P(E) + P(a(n) \\in A | \\overline{E}) P(\\overline{E})\\\\ &= \\frac{1}{2} \\cdot \\frac{1}{2} + 0 \\cdot \\frac{1}{2}\\\\ &= \\frac{1}{4}. \\end{align*}\nThe probability of $a(n)$ being chosen if $n$ is odd is exactly the same, so the probability of $a(n)$ being chosen is $\\frac{1}{4}$ if $n$ is even and $\\frac{3}{4}$ if $n$ is odd.\nThe probability of $a(n)$ being chosen is $\\frac{1}{n}$. The probability of $"}
{"epoch": 1, "global_step": 927, "prompt_id": 1, "prompt": "Explain why GPUs are not just \"faster CPUs\" for training neural networks—discuss memory bandwidth, batching, kernel fusion, and communication, at a level where someone could draw a wrong cartoon and a better cartoon of what is happening.", "response": "500 words, no citations, no references, no notes, no quotes, no diagrams, no images, no code. I want a coherent, simple, and readable explanation.What is a GPU? GPUs are graphics processing units, which are a type of coprocessor used for performing calculations in parallel, rather than sequentially, as a CPU does. They are typically used for rendering graphics and video, and more recently, they have been used for machine learning and deep learning. GPUs are optimized for parallel processing, making them particularly well-suited for tasks that require many computations, such as neural network training, image processing, and data analysis. GPUs typically have many cores, which can work in parallel, and are designed to perform mathematical operations like matrix multiplication, which are essential for neural network training. GPUs have a high bandwidth and fast memory access, which enables them to handle large datasets efficiently. GPUs also have a specialized architecture that makes them well-suited for parallel processing, allowing them to perform multiple computations at once. GPUs are not just \"faster CPUs\" for training neural networks, but they are a specialized hardware designed for parallel processing and have advantages over CPUs in terms of memory bandwidth, batching, kernel fusion, and communication. GPU vs. CPU for Neural Network Training: Memory Bandwidth: GPUs have a much higher bandwidth compared to CPUs, which means they can process data faster and handle large datasets more efficiently. CPUs, on the other hand, have a limited bandwidth, which can become a bottleneck when working with large datasets. Batching: GPUs can work with larger batches of data, which can significantly speed up the training process. CPUs, on the other hand, have a limit on the size of batches they can work with, making GPUs more efficient in terms of training time. Kernel Fusion: GPUs have a specialized architecture that allows for kernel fusion, which means that multiple instructions can be fused into a single operation, resulting in faster execution. CPUs, on the other hand, are not as efficient in terms of kernel fusion, making GPUs more efficient in terms of performance. Communication: GPUs have a specialized architecture that makes them more efficient in terms of communication, allowing them to communicate with each other more efficiently. CPUs, on the other hand, have a limited communication capacity, which can become a bottleneck when working with large datasets. GPUs are not just \"faster CPUs\" for training neural networks, but they have specialized hardware and architecture that make them more efficient in terms of memory bandwidth, batching, kernel fusion, and communication, which allows them"}
{"epoch": 1, "global_step": 927, "prompt_id": 2, "prompt": "Some philosophers defend views that feel almost like performance art. Explain—as if to a curious undergrad—three different interpretations of what serious philosophers might be doing when they advocate bizarre-sounding positions (truth-seeking, steelmanning, career incentives, dialectical usefulness, etc.).", "response": "How would you respond to someone who thinks that these views are either uninteresting or incoherent?\nWhat is a serious philosopher?\nA serious philosopher is one who is not afraid to express a bold, controversial, or radical view. A serious philosopher is one who is not afraid to be wrong. A serious philosopher is one who is not afraid to be thought of as foolish, or eccentric, or even crazy. A serious philosopher is one who is not afraid to be ridiculed, or reviled, or reviled and ridiculed.\nWhat is a serious philosopher’s job?\nThe job of a serious philosopher is to find the truth. The job of a serious philosopher is to find the truth about reality. The job of a serious philosopher is to find the truth about the universe. The job of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s work?\nThe work of a serious philosopher is to find the truth. The work of a serious philosopher is to find the truth about reality. The work of a serious philosopher is to find the truth about the universe. The work of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s aim?\nThe aim of a serious philosopher is to find the truth. The aim of a serious philosopher is to find the truth about reality. The aim of a serious philosopher is to find the truth about the universe. The aim of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s goal?\nThe goal of a serious philosopher is to find the truth. The goal of a serious philosopher is to find the truth about reality. The goal of a serious philosopher is to find the truth about the universe. The goal of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s task?\nThe task of a serious philosopher is to find the truth. The task of a serious philosopher is to find the truth about reality. The task of a serious philosopher is to find the truth about the universe. The task of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s mission?\nThe mission of a serious philosopher is to find the truth. The mission of a serious philosopher is to find the truth about reality. The mission of a serious philosopher is to find the truth about the universe. The mission of a serious philosopher is to find the truth about the world.\nWhat is a serious philosopher’s role?\nThe role of a serious philosopher is to find the truth. The"}
{"epoch": 1, "global_step": 927, "prompt_id": 3, "prompt": "You are first author on a messy multi-lab paper; deadlines slip and people ghost you until the last minute but rarely say \"no.\" Outline a delegation and communication system that is kind, repeatability-based, and escalates without humiliating juniors.", "response": "This is a very common issue. When I was a postdoc, I was the first author on a 10-author paper. It was a mess. People were ghosting me and I was ghosting people. It was not a great experience.\nI'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this? I'm the PI of a grant that is funded by a lot of agencies. I have 4 co-PIs on my grant, but I'm the only one who is responsible for reporting progress to the agencies. My co-PIs don't understand this and are frustrated. What's a good way to manage this?\nAs a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper? As a postdoc, I'm the first author on a 20+ author paper, and it's taking longer than I expected. What's the best way to manage my expectations for this paper?\nHow can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be. How can I be more assertive with my PI without it seeming like I'm being a jerk? I've been a postdoc for almost a year, and I'm still a little shy about telling my PI that I want to do a certain project or that I'm not happy with the way something is going. I feel like I'm not being as assertive as I should be.\nI've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this? I've been a postdoc for 2 years, and I'm still in the dark about how I'll be evaluated for promotion. Is there a way to get information about this?\nI have a new postdoc, and I need to write a new lab manual. How do I"}
{"epoch": 1, "global_step": 927, "prompt_id": 4, "prompt": "You suddenly need to teach a semester of project-first high school \"engineering\" to beginners, with a shoestring budget. Propose a progression of hands-on milestones (weeks 1–16 mindset, not a shopping list) that could end in something ambitious without setting everyone up for only frustration.", "response": "What would you do, and what would you keep in mind? And how would you scale the same approach to teaching a full semester of \"engineering\" at a high school?\nWe're in a situation where we have a few weeks to put together a semester-long project-first class at a high school, and I'm asking for a bit of guidance. The project will be a high-altitude weather balloon launch, with a payload that could include a GPS, a temperature sensor, a barometer, a camera, and a small LED (or perhaps a few LEDs). The students will build and launch the balloon, and then analyze the data they collect. We're not building a rocket, but we're also not doing a simple balloon launch with a sensor attached.\nWhat we have:\nA school with a few dozen high school students (ages 14–18) who have had little to no engineering education, but who are interested in engineering and are motivated by the prospect of building something, launching it, and analyzing the data.\nA few hundred dollars for supplies (some of which will come from the school, but not all).\nA 3D printer that's not particularly fast, but will be adequate for the first version of the balloon.\nA few hundred feet of PVC pipe (and associated fittings) that will be used to build the balloon.\nA 6V battery (perhaps 1000 mAh) that will be used to power the payload.\nA few hundred feet of 1/4\" copper wire that will be used for the payload's antenna.\nThe students will be doing this on their own time, in their free time, and in their spare time.\nA few weeks to build and launch the balloon.\nA semester-long class (or a semester of classes, depending on how we structure the class).\nA few hundred feet of 1/4\" copper wire that will be used for the payload's antenna.\nA 6V battery (perhaps 1000 mAh) that will be used to power the payload.\nA few hundred feet of 1/4\" copper wire that will be used for the payload's antenna.\nA 6V battery (perhaps 1000 mAh) that will be used to power the payload.\nA few hundred feet of 1/4\" copper wire that will be used for the payload's antenna.\nA 6V battery (perhaps 1000 mAh) that will be used to power the payload.\nA few hundred feet of 1/4\" copper wire that will be used"}
{"epoch": 1, "global_step": 927, "prompt_id": 5, "prompt": "Write a 250-word story where the twist hinges on a misunderstanding of conditional probability.", "response": "You may use a standard conditional probability formula, but you must show the reader how to understand the relationship.\nExample: Two brothers, Jack and Jill, are trying to decide which of them will marry the woman they both like. If Jill marries the woman, then Jack will not, and vice versa. If Jack marries the woman, then Jill will not. The brothers know that the probability that Jill will marry the woman is 0.7, and that the probability that Jack will marry the woman is 0.4. Which brother will marry the woman?\nSolution: We can represent this scenario using the following Venn diagram:\nThe probability that Jill will marry the woman is 0.7, so the shaded region is 0.7. The probability that Jack will marry the woman is 0.4, so the shaded region is 0.4. The probability that Jill will marry the woman is 0.7, and the probability that Jack will marry the woman is 0.4, so the probability that neither brother will marry the woman is 1 - 0.7 - 0.4 = 0.9. This means that the probability that one brother will marry the woman is 0.1, so the probability that Jack will marry the woman is 0.1. Therefore, Jack will marry the woman.\nIn your story, you should describe a scenario where the twist hinges on a misunderstanding of conditional probability. You should show the reader how to understand the relationship between the conditional probability and the probability of the event in question.\nThe story should be written in 250 words or less, and it should be in the form of a short story or a poem. You should include a title and a brief description of the story. You should also include a reference to the conditional probability formula and how it was used to solve the problem.\nFor example, here's a story that uses a misunderstanding of conditional probability as the twist:\nA man named John was trying to decide whether to take a job in a new city. He had two options: Option 1: Take the job and move to the new city, or Option 2: Stay in his current city and continue working there. He knew that the probability of getting a job in the new city was 0.8, and the probability of getting a job in his current city was 0.2. He also knew that if he took the job in the new city, then he would have to move to the new city, and if"}
{"epoch": 1, "global_step": 927, "prompt_id": 6, "prompt": "Engineering students debate whether chasing all-A grades is worth it versus projects and sleep. Analyze what grades do and do not proxy for, first-job signaling, learning depth, and opportunity cost—end with conditional advice for different student goals.", "response": "(10 minutes)\nGrading is an important, but often misunderstood, part of your education. In this episode, we discuss the differences between high school, college, and grad school grading systems, the importance of grading feedback, and how to optimize your grades in each system. (20 minutes)\nIn this episode, we explore the different types of grades that universities use, including cumulative GPA, class grades, and pass/fail. We discuss the importance of understanding the grading system at your university and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, and weighted GPA. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the importance of understanding the grading system at your university, including the difference between cumulative GPA and class grades, the impact of weighted and unweighted GPAs, and how to manage your grade point average. (20 minutes)\nIn this episode, we explore the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also discuss the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore the importance of understanding the grading system at your high school and how to navigate it effectively. (20 minutes)\nIn this episode, we discuss the different types of grades that high schools use, including letter grades, GPA, weighted GPA, and honors classes. We also explore"}
{"epoch": 1, "global_step": 927, "prompt_id": 7, "prompt": "You are a 10x engineer in a startup with a legacy monolith. You want to build a new system, but the existing code is a spaghetti monster. How do you start? Outline a plan to refactor the codebase without rewriting everything.", "response": "You will need to think about the following:\n\n- How do you extract a single class out of the codebase?\n- How do you test your code?\n- How do you keep the existing functionality intact while you refactor?\n- How do you refactor the code in the most maintainable way possible?\n- How do you communicate with your team about the refactor process?\n\nIn this kata, you will write a function that refactors a given JavaScript code snippet to make it more maintainable and testable. You will need to use the following techniques:\n\n- Refactoring\n- Extracting classes\n- Extracting methods\n- Extracting functions\n- Refactoring with tests\n- Communicating with your team\n- Working in a team\n\nThis kata is a practical application of the principles of test-driven development and code refactoring. You will need to apply these principles to a real-world scenario, which will help you to understand how to apply them in practice.\n\nYou will also need to use the following tools:\n\n- A code editor (such as VS Code, Sublime Text, or Atom)\n- A version control system (such as Git or Bitbucket)\n- A testing framework (such as Mocha, Jest, or Jest)\n\nThis kata will take you about 2-3 hours to complete, depending on your experience and the complexity of the code snippet you start with. You can use the following resources to help you:\n\n- [Refactoring](https://en.wikipedia.org/wiki/Code_refactoring)\n- [Test-Driven Development](https://en.wikipedia.org/wiki/Test-driven_development)\n- [Extracting classes](https://en.wikipedia.org/wiki/Extract_class)\n- [Extracting methods](https://en.wikipedia.org/wiki/Extract_method)\n- [Extracting functions](https://en.wikipedia.org/wiki/Extract_function)\n- [Refactoring with tests](https://en.wikipedia.org/wiki/Test-driven_development#Refactoring)\n- [Communicating with your team](https://en.wikipedia.org/wiki/Communication)\n- [Working in a team](https://en.wikipedia.org/wiki/Teamwork)\n\nGood luck and have fun!\nIsolate the classes in the given code snippet:\n\n```javascript\nconst app = {\n state: {\n user: {\n name: 'John',\n age: 30\n },\n posts: [\n {\n id: 1,\n title: 'My first post',\n content: 'This is my first post.'\n },\n {\n id: 2,\n title: '"}

3
runs/Jul30_03-28-29_scw-zen-knuth/events.out.tfevents.1785382176.scw-zen-knuth.183882.0 Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:33fb528865fdcdc98cbc60ff2937edf02d796964cb44fd40e9373969272d0b24
size 112608

3
runs/Jul30_03-28-29_scw-zen-knuth/events.out.tfevents.1785396884.scw-zen-knuth.183882.1 Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:29494368a59122aee4d79f3d624cf37e087dd8b76cb23c2bc55bed8ee3d16792
size 1259

26
special_tokens_map.json Normal file
View File

@@ -0,0 +1,26 @@
{
"additional_special_tokens": [
{
"content": "<|eom_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
],
"bos_token": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "<|eot_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "<|eot_id|>"
}

95
stability_metrics.jsonl Normal file
View File

@@ -0,0 +1,95 @@
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.434472680091858, "stability/repetition_rate_mean": 0.8257812261581421, "step": 20}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.364843726158142, "stability/repetition_rate_mean": 0.8824218511581421, "step": 30}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.0068359375, "stability/repetition_rate_mean": 0.8031250238418579, "step": 40}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.07568359375, "stability/repetition_rate_mean": 0.8675781488418579, "step": 50}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2527344226837158, "stability/repetition_rate_mean": 0.7318359613418579, "step": 60}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3330078125, "stability/repetition_rate_mean": 0.8822265863418579, "step": 70}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.825048804283142, "stability/repetition_rate_mean": 0.8583984375, "step": 80}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.663671851158142, "stability/repetition_rate_mean": 0.8740234375, "step": 90}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4281249046325684, "stability/repetition_rate_mean": 0.9400390386581421, "step": 100}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.246875047683716, "stability/repetition_rate_mean": 0.809374988079071, "step": 110}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.070214867591858, "stability/repetition_rate_mean": 0.7470703125, "step": 120}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4833984375, "stability/repetition_rate_mean": 0.773242175579071, "step": 130}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.019140601158142, "stability/repetition_rate_mean": 0.8072265386581421, "step": 140}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5673828125, "stability/repetition_rate_mean": 0.807421863079071, "step": 150}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4421875476837158, "stability/repetition_rate_mean": 0.789257824420929, "step": 160}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5763671398162842, "stability/repetition_rate_mean": 0.7845703363418579, "step": 170}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5693359375, "stability/repetition_rate_mean": 0.7490234375, "step": 180}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.1296875476837158, "stability/repetition_rate_mean": 0.850781261920929, "step": 190}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4445312023162842, "stability/repetition_rate_mean": 0.796679675579071, "step": 200}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3254883289337158, "stability/repetition_rate_mean": 0.81640625, "step": 210}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.763671875, "stability/repetition_rate_mean": 0.7367187738418579, "step": 220}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4400391578674316, "stability/repetition_rate_mean": 0.8841797113418579, "step": 230}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.0635743141174316, "stability/repetition_rate_mean": 0.802539050579071, "step": 240}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.45166015625, "stability/repetition_rate_mean": 0.811718761920929, "step": 250}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.894628882408142, "stability/repetition_rate_mean": 0.813281238079071, "step": 260}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.030859351158142, "stability/repetition_rate_mean": 0.828906238079071, "step": 270}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.202734351158142, "stability/repetition_rate_mean": 0.8851562738418579, "step": 280}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6193358898162842, "stability/repetition_rate_mean": 0.8099609613418579, "step": 290}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1236329078674316, "stability/repetition_rate_mean": 0.7289062738418579, "step": 300}
{"eval_stability/response_length_mean": 512.0, "eval_stability/response_length_std": 0.0, "eval_stability/response_length_var": 0.0, "eval_stability/token_entropy_mean": 1.9105956554412842, "eval_stability/repetition_rate_mean": 0.9048827886581421, "step": 300}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.908203125, "stability/repetition_rate_mean": 0.799023449420929, "step": 310}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.083886742591858, "stability/repetition_rate_mean": 0.9072265625, "step": 320}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7205078601837158, "stability/repetition_rate_mean": 0.955273449420929, "step": 330}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.8572266101837158, "stability/repetition_rate_mean": 0.7232421636581421, "step": 340}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2517578601837158, "stability/repetition_rate_mean": 0.76953125, "step": 350}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2664062976837158, "stability/repetition_rate_mean": 0.889843761920929, "step": 360}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.752539038658142, "stability/repetition_rate_mean": 0.777148425579071, "step": 370}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.844921827316284, "stability/repetition_rate_mean": 0.7945312261581421, "step": 380}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.869531273841858, "stability/repetition_rate_mean": 0.74609375, "step": 390}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6525390148162842, "stability/repetition_rate_mean": 0.7679687738418579, "step": 400}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6925780773162842, "stability/repetition_rate_mean": 0.854296863079071, "step": 410}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1460938453674316, "stability/repetition_rate_mean": 0.822460949420929, "step": 420}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.808789014816284, "stability/repetition_rate_mean": 0.78125, "step": 430}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.164990186691284, "stability/repetition_rate_mean": 0.8707031011581421, "step": 440}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7429687976837158, "stability/repetition_rate_mean": 0.8236328363418579, "step": 450}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7371094226837158, "stability/repetition_rate_mean": 0.8646484613418579, "step": 460}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.244531273841858, "stability/repetition_rate_mean": 0.8687499761581421, "step": 470}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1624999046325684, "stability/repetition_rate_mean": 0.8519531488418579, "step": 480}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9038817882537842, "stability/repetition_rate_mean": 0.8125, "step": 490}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1546874046325684, "stability/repetition_rate_mean": 0.7855468988418579, "step": 500}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.330175757408142, "stability/repetition_rate_mean": 0.8619140386581421, "step": 510}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.715429663658142, "stability/repetition_rate_mean": 0.856640636920929, "step": 520}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.365820288658142, "stability/repetition_rate_mean": 0.7701171636581421, "step": 530}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7644531726837158, "stability/repetition_rate_mean": 0.8062499761581421, "step": 540}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9962890148162842, "stability/repetition_rate_mean": 0.8472656011581421, "step": 550}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4849610328674316, "stability/repetition_rate_mean": 0.784960925579071, "step": 560}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.817578136920929, "stability/repetition_rate_mean": 0.828906238079071, "step": 570}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6736328601837158, "stability/repetition_rate_mean": 0.7662109136581421, "step": 580}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.351171851158142, "stability/repetition_rate_mean": 0.8466796875, "step": 590}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.7417969703674316, "stability/repetition_rate_mean": 0.8042968511581421, "step": 600}
{"eval_stability/response_length_mean": 512.0, "eval_stability/response_length_std": 0.0, "eval_stability/response_length_var": 0.0, "eval_stability/token_entropy_mean": 2.07421875, "eval_stability/repetition_rate_mean": 0.8873046636581421, "step": 600}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9982421398162842, "stability/repetition_rate_mean": 0.744921863079071, "step": 610}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.2964844703674316, "stability/repetition_rate_mean": 0.8783203363418579, "step": 620}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.377539038658142, "stability/repetition_rate_mean": 0.8720703125, "step": 630}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7732422351837158, "stability/repetition_rate_mean": 0.7818359136581421, "step": 640}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5888671875, "stability/repetition_rate_mean": 0.850390613079071, "step": 650}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.3021483421325684, "stability/repetition_rate_mean": 0.775585949420929, "step": 660}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6103515625, "stability/repetition_rate_mean": 0.785937488079071, "step": 670}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3708984851837158, "stability/repetition_rate_mean": 0.7417968511581421, "step": 680}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.596289038658142, "stability/repetition_rate_mean": 0.8603515625, "step": 690}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.692675828933716, "stability/repetition_rate_mean": 0.8111327886581421, "step": 700}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.209765672683716, "stability/repetition_rate_mean": 0.840039074420929, "step": 710}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.8656250238418579, "stability/repetition_rate_mean": 0.8203125, "step": 720}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.055468797683716, "stability/repetition_rate_mean": 0.7437499761581421, "step": 730}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.513671875, "stability/repetition_rate_mean": 0.775195300579071, "step": 740}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7333984375, "stability/repetition_rate_mean": 0.825390636920929, "step": 750}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.017578125, "stability/repetition_rate_mean": 0.779101550579071, "step": 760}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.833984375, "stability/repetition_rate_mean": 0.8814452886581421, "step": 770}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.575585961341858, "stability/repetition_rate_mean": 0.795703113079071, "step": 780}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.439843773841858, "stability/repetition_rate_mean": 0.8095703125, "step": 790}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.61376953125, "stability/repetition_rate_mean": 0.9134765863418579, "step": 800}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4040038585662842, "stability/repetition_rate_mean": 0.8667968511581421, "step": 810}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.8826172351837158, "stability/repetition_rate_mean": 0.9224609136581421, "step": 820}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1563477516174316, "stability/repetition_rate_mean": 0.8330078125, "step": 830}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4929687976837158, "stability/repetition_rate_mean": 0.8158203363418579, "step": 840}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3533203601837158, "stability/repetition_rate_mean": 0.7544921636581421, "step": 850}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.556640625, "stability/repetition_rate_mean": 0.805468738079071, "step": 860}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4718749523162842, "stability/repetition_rate_mean": 0.864453136920929, "step": 870}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.583984375, "stability/repetition_rate_mean": 0.8818359375, "step": 880}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.6566405296325684, "stability/repetition_rate_mean": 0.8443359136581421, "step": 890}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3954589366912842, "stability/repetition_rate_mean": 0.7369140386581421, "step": 900}
{"eval_stability/response_length_mean": 512.0, "eval_stability/response_length_std": 0.0, "eval_stability/response_length_var": 0.0, "eval_stability/token_entropy_mean": 1.127832055091858, "eval_stability/repetition_rate_mean": 0.811718761920929, "step": 900}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.468652367591858, "stability/repetition_rate_mean": 0.875195324420929, "step": 910}
{"stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.8033204078674316, "stability/repetition_rate_mean": 0.861328125, "step": 920}
{"eval_stability/response_length_mean": 512.0, "eval_stability/response_length_std": 0.0, "eval_stability/response_length_var": 0.0, "eval_stability/token_entropy_mean": 1.601953148841858, "eval_stability/repetition_rate_mean": 0.8705078363418579, "step": 927}

3
tokenizer.json Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:76cfe2f054560aae896b2b75e273dc97a39e304d4ad19c44a9727a1d6b33c4cc
size 17210021

2068
tokenizer_config.json Normal file
View File

File diff suppressed because it is too large Load Diff

8
train_results.json Normal file
View File

@@ -0,0 +1,8 @@
{
"epoch": 1.0,
"total_flos": 1.761935585720664e+18,
"train_loss": 0.5203882457111775,
"train_runtime": 14354.8151,
"train_samples_per_second": 4.13,
"train_steps_per_second": 0.065
}

96
trainer_log.jsonl Normal file
View File

@@ -0,0 +1,96 @@
{"current_steps": 10, "total_steps": 927, "loss": 0.6928, "accuracy": 0.40937501192092896, "lr": 4.8387096774193546e-08, "epoch": 0.010794764539198488, "percentage": 1.08, "elapsed_time": "0:02:19", "remaining_time": "3:33:40", "rewards/chosen": 0.0015822252025827765, "rewards/rejected": -0.0001372337428620085, "rewards/accuracies": 0.40937501192092896, "rewards/margins": 0.0017194593092426658, "logps/chosen": -328.8013610839844, "logps/rejected": -253.98362731933594, "logits/chosen": -1.4572813510894775, "logits/rejected": -1.5238107442855835}
{"current_steps": 20, "total_steps": 927, "loss": 0.6933, "accuracy": 0.5140625238418579, "lr": 1.0215053763440861e-07, "epoch": 0.021589529078396976, "percentage": 2.16, "elapsed_time": "0:05:11", "remaining_time": "3:55:06", "rewards/chosen": 0.0026884928811341524, "rewards/rejected": 0.0015687048435211182, "rewards/accuracies": 0.5140625238418579, "rewards/margins": 0.0011197883868589997, "logps/chosen": -329.26373291015625, "logps/rejected": -269.0686950683594, "logits/chosen": -1.4601737260818481, "logits/rejected": -1.495823860168457, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.434472680091858, "stability/repetition_rate_mean": 0.8257812261581421}
{"current_steps": 30, "total_steps": 927, "loss": 0.6925, "accuracy": 0.5140625238418579, "lr": 1.5591397849462365e-07, "epoch": 0.03238429361759547, "percentage": 3.24, "elapsed_time": "0:07:45", "remaining_time": "3:51:52", "rewards/chosen": 0.004414338618516922, "rewards/rejected": 0.0018770955502986908, "rewards/accuracies": 0.5140625238418579, "rewards/margins": 0.0025372428353875875, "logps/chosen": -316.17425537109375, "logps/rejected": -256.8540954589844, "logits/chosen": -1.4658056497573853, "logits/rejected": -1.5197511911392212, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.364843726158142, "stability/repetition_rate_mean": 0.8824218511581421}
{"current_steps": 40, "total_steps": 927, "loss": 0.6913, "accuracy": 0.550000011920929, "lr": 2.0967741935483871e-07, "epoch": 0.04317905815679395, "percentage": 4.31, "elapsed_time": "0:10:13", "remaining_time": "3:46:54", "rewards/chosen": 0.005997015163302422, "rewards/rejected": 0.0007881380734033883, "rewards/accuracies": 0.550000011920929, "rewards/margins": 0.005208877846598625, "logps/chosen": -329.3841247558594, "logps/rejected": -258.57672119140625, "logits/chosen": -1.4785693883895874, "logits/rejected": -1.5407034158706665, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.0068359375, "stability/repetition_rate_mean": 0.8031250238418579}
{"current_steps": 50, "total_steps": 927, "loss": 0.6885, "accuracy": 0.546875, "lr": 2.6344086021505376e-07, "epoch": 0.05397382269599244, "percentage": 5.39, "elapsed_time": "0:12:50", "remaining_time": "3:45:17", "rewards/chosen": 0.01573883555829525, "rewards/rejected": 0.005042524076998234, "rewards/accuracies": 0.546875, "rewards/margins": 0.010696313343942165, "logps/chosen": -313.481689453125, "logps/rejected": -258.3254089355469, "logits/chosen": -1.438301682472229, "logits/rejected": -1.503927230834961, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.07568359375, "stability/repetition_rate_mean": 0.8675781488418579}
{"current_steps": 60, "total_steps": 927, "loss": 0.6846, "accuracy": 0.604687511920929, "lr": 3.172043010752688e-07, "epoch": 0.06476858723519094, "percentage": 6.47, "elapsed_time": "0:15:12", "remaining_time": "3:39:41", "rewards/chosen": 0.02920054830610752, "rewards/rejected": 0.009999553672969341, "rewards/accuracies": 0.604687511920929, "rewards/margins": 0.019200993701815605, "logps/chosen": -318.685302734375, "logps/rejected": -265.6797180175781, "logits/chosen": -1.4596624374389648, "logits/rejected": -1.501914620399475, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2527344226837158, "stability/repetition_rate_mean": 0.7318359613418579}
{"current_steps": 70, "total_steps": 927, "loss": 0.6787, "accuracy": 0.609375, "lr": 3.7096774193548384e-07, "epoch": 0.07556335177438941, "percentage": 7.55, "elapsed_time": "0:17:38", "remaining_time": "3:35:59", "rewards/chosen": 0.06427477300167084, "rewards/rejected": 0.03113069012761116, "rewards/accuracies": 0.609375, "rewards/margins": 0.03314407169818878, "logps/chosen": -339.0155334472656, "logps/rejected": -273.65655517578125, "logits/chosen": -1.5026830434799194, "logits/rejected": -1.5432065725326538, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3330078125, "stability/repetition_rate_mean": 0.8822265863418579}
{"current_steps": 80, "total_steps": 927, "loss": 0.6616, "accuracy": 0.643750011920929, "lr": 4.247311827956989e-07, "epoch": 0.0863581163135879, "percentage": 8.63, "elapsed_time": "0:20:03", "remaining_time": "3:32:19", "rewards/chosen": 0.1002170592546463, "rewards/rejected": 0.028648091480135918, "rewards/accuracies": 0.643750011920929, "rewards/margins": 0.07156896591186523, "logps/chosen": -325.33526611328125, "logps/rejected": -254.3518524169922, "logits/chosen": -1.4859027862548828, "logits/rejected": -1.5006340742111206, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.825048804283142, "stability/repetition_rate_mean": 0.8583984375}
{"current_steps": 90, "total_steps": 927, "loss": 0.6497, "accuracy": 0.6953125, "lr": 4.78494623655914e-07, "epoch": 0.0971528808527864, "percentage": 9.71, "elapsed_time": "0:22:27", "remaining_time": "3:28:53", "rewards/chosen": 0.14759615063667297, "rewards/rejected": 0.04552074521780014, "rewards/accuracies": 0.6953125, "rewards/margins": 0.10207539796829224, "logps/chosen": -295.8207702636719, "logps/rejected": -248.85952758789062, "logits/chosen": -1.4689861536026, "logits/rejected": -1.4905648231506348, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.663671851158142, "stability/repetition_rate_mean": 0.8740234375}
{"current_steps": 100, "total_steps": 927, "loss": 0.6413, "accuracy": 0.6890625357627869, "lr": 4.999361498869529e-07, "epoch": 0.10794764539198488, "percentage": 10.79, "elapsed_time": "0:24:49", "remaining_time": "3:25:18", "rewards/chosen": 0.19397814571857452, "rewards/rejected": 0.06464159488677979, "rewards/accuracies": 0.6890625357627869, "rewards/margins": 0.12933655083179474, "logps/chosen": -314.435791015625, "logps/rejected": -249.51695251464844, "logits/chosen": -1.4919625520706177, "logits/rejected": -1.5506508350372314, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4281249046325684, "stability/repetition_rate_mean": 0.9400390386581421}
{"current_steps": 110, "total_steps": 927, "loss": 0.6214, "accuracy": 0.714062511920929, "lr": 4.995460728562402e-07, "epoch": 0.11874240993118337, "percentage": 11.87, "elapsed_time": "0:27:12", "remaining_time": "3:22:04", "rewards/chosen": 0.24892178177833557, "rewards/rejected": 0.06499166041612625, "rewards/accuracies": 0.714062511920929, "rewards/margins": 0.18393009901046753, "logps/chosen": -319.29156494140625, "logps/rejected": -253.4399871826172, "logits/chosen": -1.4327154159545898, "logits/rejected": -1.5077978372573853, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.246875047683716, "stability/repetition_rate_mean": 0.809374988079071}
{"current_steps": 120, "total_steps": 927, "loss": 0.6032, "accuracy": 0.739062488079071, "lr": 4.988019438437758e-07, "epoch": 0.12953717447038188, "percentage": 12.94, "elapsed_time": "0:29:35", "remaining_time": "3:18:59", "rewards/chosen": 0.3554464876651764, "rewards/rejected": 0.1081392914056778, "rewards/accuracies": 0.739062488079071, "rewards/margins": 0.2473072111606598, "logps/chosen": -330.7845764160156, "logps/rejected": -248.1776885986328, "logits/chosen": -1.4564826488494873, "logits/rejected": -1.5484386682510376, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.070214867591858, "stability/repetition_rate_mean": 0.7470703125}
{"current_steps": 130, "total_steps": 927, "loss": 0.5951, "accuracy": 0.721875011920929, "lr": 4.977048186079155e-07, "epoch": 0.14033193900958035, "percentage": 14.02, "elapsed_time": "0:31:58", "remaining_time": "3:16:00", "rewards/chosen": 0.40177080035209656, "rewards/rejected": 0.11879038065671921, "rewards/accuracies": 0.721875011920929, "rewards/margins": 0.28298041224479675, "logps/chosen": -317.5703125, "logps/rejected": -253.7754364013672, "logits/chosen": -1.4577782154083252, "logits/rejected": -1.508943796157837, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4833984375, "stability/repetition_rate_mean": 0.773242175579071}
{"current_steps": 140, "total_steps": 927, "loss": 0.6036, "accuracy": 0.6890625357627869, "lr": 4.962562537324176e-07, "epoch": 0.15112670354877883, "percentage": 15.1, "elapsed_time": "0:34:25", "remaining_time": "3:13:31", "rewards/chosen": 0.42533954977989197, "rewards/rejected": 0.1358480602502823, "rewards/accuracies": 0.6890625357627869, "rewards/margins": 0.2894915044307709, "logps/chosen": -343.68548583984375, "logps/rejected": -272.2403869628906, "logits/chosen": -1.4774430990219116, "logits/rejected": -1.512833833694458, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.019140601158142, "stability/repetition_rate_mean": 0.8072265386581421}
{"current_steps": 150, "total_steps": 927, "loss": 0.6074, "accuracy": 0.6968750357627869, "lr": 4.944583044179871e-07, "epoch": 0.16192146808797733, "percentage": 16.18, "elapsed_time": "0:36:48", "remaining_time": "3:10:40", "rewards/chosen": 0.412137508392334, "rewards/rejected": 0.13995173573493958, "rewards/accuracies": 0.6968750357627869, "rewards/margins": 0.2721858024597168, "logps/chosen": -320.7796936035156, "logps/rejected": -268.3492126464844, "logits/chosen": -1.4802706241607666, "logits/rejected": -1.5120983123779297, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5673828125, "stability/repetition_rate_mean": 0.807421863079071}
{"current_steps": 160, "total_steps": 927, "loss": 0.5807, "accuracy": 0.7484375238418579, "lr": 4.923135215663896e-07, "epoch": 0.1727162326271758, "percentage": 17.26, "elapsed_time": "0:39:12", "remaining_time": "3:07:56", "rewards/chosen": 0.46426326036453247, "rewards/rejected": 0.09919857233762741, "rewards/accuracies": 0.7484375238418579, "rewards/margins": 0.3650646209716797, "logps/chosen": -317.5917663574219, "logps/rejected": -243.84231567382812, "logits/chosen": -1.479994773864746, "logits/rejected": -1.5399045944213867, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4421875476837158, "stability/repetition_rate_mean": 0.789257824420929}
{"current_steps": 170, "total_steps": 927, "loss": 0.5752, "accuracy": 0.706250011920929, "lr": 4.89824948161273e-07, "epoch": 0.1835109971663743, "percentage": 18.34, "elapsed_time": "0:41:34", "remaining_time": "3:05:06", "rewards/chosen": 0.47893524169921875, "rewards/rejected": 0.08635219186544418, "rewards/accuracies": 0.706250011920929, "rewards/margins": 0.39258310198783875, "logps/chosen": -308.2352600097656, "logps/rejected": -256.36358642578125, "logits/chosen": -1.4407405853271484, "logits/rejected": -1.5115913152694702, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5763671398162842, "stability/repetition_rate_mean": 0.7845703363418579}
{"current_steps": 180, "total_steps": 927, "loss": 0.5744, "accuracy": 0.7109375, "lr": 4.8699611495083e-07, "epoch": 0.1943057617055728, "percentage": 19.42, "elapsed_time": "0:43:58", "remaining_time": "3:02:31", "rewards/chosen": 0.5145285725593567, "rewards/rejected": 0.10545291751623154, "rewards/accuracies": 0.7109375, "rewards/margins": 0.40907564759254456, "logps/chosen": -317.1926574707031, "logps/rejected": -261.8941650390625, "logits/chosen": -1.504378318786621, "logits/rejected": -1.5372484922409058, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5693359375, "stability/repetition_rate_mean": 0.7490234375}
{"current_steps": 190, "total_steps": 927, "loss": 0.5629, "accuracy": 0.707812488079071, "lr": 4.838310354384302e-07, "epoch": 0.2051005262447713, "percentage": 20.5, "elapsed_time": "0:46:25", "remaining_time": "3:00:05", "rewards/chosen": 0.5523476600646973, "rewards/rejected": 0.11809053272008896, "rewards/accuracies": 0.707812488079071, "rewards/margins": 0.4342571198940277, "logps/chosen": -307.32684326171875, "logps/rejected": -263.76373291015625, "logits/chosen": -1.4963879585266113, "logits/rejected": -1.5239351987838745, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.1296875476837158, "stability/repetition_rate_mean": 0.850781261920929}
{"current_steps": 200, "total_steps": 927, "loss": 0.5524, "accuracy": 0.745312511920929, "lr": 4.803342001883246e-07, "epoch": 0.21589529078396977, "percentage": 21.57, "elapsed_time": "0:48:54", "remaining_time": "2:57:45", "rewards/chosen": 0.556470513343811, "rewards/rejected": 0.09783537685871124, "rewards/accuracies": 0.745312511920929, "rewards/margins": 0.4586350917816162, "logps/chosen": -318.7608337402344, "logps/rejected": -267.5777282714844, "logits/chosen": -1.5078216791152954, "logits/rejected": -1.541197419166565, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4445312023162842, "stability/repetition_rate_mean": 0.796679675579071}
{"current_steps": 210, "total_steps": 927, "loss": 0.5515, "accuracy": 0.7484375238418579, "lr": 4.7651057045450515e-07, "epoch": 0.22669005532316827, "percentage": 22.65, "elapsed_time": "0:51:13", "remaining_time": "2:54:54", "rewards/chosen": 0.5690556764602661, "rewards/rejected": 0.10906264930963516, "rewards/accuracies": 0.7484375238418579, "rewards/margins": 0.4599929749965668, "logps/chosen": -326.5751647949219, "logps/rejected": -245.41970825195312, "logits/chosen": -1.4529699087142944, "logits/rejected": -1.5274174213409424, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3254883289337158, "stability/repetition_rate_mean": 0.81640625}
{"current_steps": 220, "total_steps": 927, "loss": 0.5548, "accuracy": 0.746874988079071, "lr": 4.72365571141757e-07, "epoch": 0.23748481986236675, "percentage": 23.73, "elapsed_time": "0:53:36", "remaining_time": "2:52:17", "rewards/chosen": 0.5794717669487, "rewards/rejected": 0.08195456117391586, "rewards/accuracies": 0.746874988079071, "rewards/margins": 0.4975171983242035, "logps/chosen": -331.7127380371094, "logps/rejected": -264.9964294433594, "logits/chosen": -1.4564087390899658, "logits/rejected": -1.4975181818008423, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.763671875, "stability/repetition_rate_mean": 0.7367187738418579}
{"current_steps": 230, "total_steps": 927, "loss": 0.5575, "accuracy": 0.7359375357627869, "lr": 4.6790508310889007e-07, "epoch": 0.24827958440156525, "percentage": 24.81, "elapsed_time": "0:55:53", "remaining_time": "2:49:23", "rewards/chosen": 0.5666539072990417, "rewards/rejected": 0.07812191545963287, "rewards/accuracies": 0.7359375357627869, "rewards/margins": 0.48853203654289246, "logps/chosen": -311.7873229980469, "logps/rejected": -257.8661193847656, "logits/chosen": -1.4929759502410889, "logits/rejected": -1.5188223123550415, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4400391578674316, "stability/repetition_rate_mean": 0.8841797113418579}
{"current_steps": 240, "total_steps": 927, "loss": 0.5496, "accuracy": 0.7281250357627869, "lr": 4.6313543482507056e-07, "epoch": 0.25907434894076375, "percentage": 25.89, "elapsed_time": "0:58:12", "remaining_time": "2:46:38", "rewards/chosen": 0.5762147307395935, "rewards/rejected": 0.05319035053253174, "rewards/accuracies": 0.7281250357627869, "rewards/margins": 0.5230244398117065, "logps/chosen": -319.4496154785156, "logps/rejected": -261.7314453125, "logits/chosen": -1.4668247699737549, "logits/rejected": -1.50505530834198, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.0635743141174316, "stability/repetition_rate_mean": 0.802539050579071}
{"current_steps": 250, "total_steps": 927, "loss": 0.5612, "accuracy": 0.723437488079071, "lr": 4.580633933910901e-07, "epoch": 0.26986911347996223, "percentage": 26.97, "elapsed_time": "1:00:37", "remaining_time": "2:44:10", "rewards/chosen": 0.5559987425804138, "rewards/rejected": 0.0560583770275116, "rewards/accuracies": 0.723437488079071, "rewards/margins": 0.4999403655529022, "logps/chosen": -305.2557678222656, "logps/rejected": -253.59902954101562, "logits/chosen": -1.436219334602356, "logits/rejected": -1.46418035030365, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.45166015625, "stability/repetition_rate_mean": 0.811718761920929}
{"current_steps": 260, "total_steps": 927, "loss": 0.5574, "accuracy": 0.7203125357627869, "lr": 4.526961549383108e-07, "epoch": 0.2806638780191607, "percentage": 28.05, "elapsed_time": "1:03:04", "remaining_time": "2:41:47", "rewards/chosen": 0.6053926348686218, "rewards/rejected": 0.08161897957324982, "rewards/accuracies": 0.7203125357627869, "rewards/margins": 0.5237736105918884, "logps/chosen": -325.4045104980469, "logps/rejected": -272.9486389160156, "logits/chosen": -1.4230726957321167, "logits/rejected": -1.4305095672607422, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.894628882408142, "stability/repetition_rate_mean": 0.813281238079071}
{"current_steps": 270, "total_steps": 927, "loss": 0.5356, "accuracy": 0.7437500357627869, "lr": 4.470413344189098e-07, "epoch": 0.2914586425583592, "percentage": 29.13, "elapsed_time": "1:05:33", "remaining_time": "2:39:32", "rewards/chosen": 0.6630539894104004, "rewards/rejected": 0.06447024643421173, "rewards/accuracies": 0.7437500357627869, "rewards/margins": 0.5985837578773499, "logps/chosen": -323.3600158691406, "logps/rejected": -270.2887878417969, "logits/chosen": -1.4393119812011719, "logits/rejected": -1.467215895652771, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.030859351158142, "stability/repetition_rate_mean": 0.828906238079071}
{"current_steps": 280, "total_steps": 927, "loss": 0.5221, "accuracy": 0.7671875357627869, "lr": 4.4110695480190597e-07, "epoch": 0.30225340709755766, "percentage": 30.2, "elapsed_time": "1:07:58", "remaining_time": "2:37:03", "rewards/chosen": 0.7377834916114807, "rewards/rejected": 0.10894794762134552, "rewards/accuracies": 0.7671875357627869, "rewards/margins": 0.6288355588912964, "logps/chosen": -330.3580627441406, "logps/rejected": -267.19561767578125, "logits/chosen": -1.4905976057052612, "logits/rejected": -1.5187627077102661, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.202734351158142, "stability/repetition_rate_mean": 0.8851562738418579}
{"current_steps": 290, "total_steps": 927, "loss": 0.5299, "accuracy": 0.746874988079071, "lr": 4.3490143569030017e-07, "epoch": 0.3130481716367562, "percentage": 31.28, "elapsed_time": "1:10:21", "remaining_time": "2:34:33", "rewards/chosen": 0.6983198523521423, "rewards/rejected": 0.10521648079156876, "rewards/accuracies": 0.746874988079071, "rewards/margins": 0.5931033492088318, "logps/chosen": -320.11309814453125, "logps/rejected": -252.4005584716797, "logits/chosen": -1.4678796529769897, "logits/rejected": -1.5179208517074585, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6193358898162842, "stability/repetition_rate_mean": 0.8099609613418579}
{"current_steps": 300, "total_steps": 927, "loss": 0.5031, "accuracy": 0.770312488079071, "lr": 4.284335813754769e-07, "epoch": 0.32384293617595467, "percentage": 32.36, "elapsed_time": "1:12:46", "remaining_time": "2:32:06", "rewards/chosen": 0.6781904101371765, "rewards/rejected": 9.752810001373291e-06, "rewards/accuracies": 0.770312488079071, "rewards/margins": 0.6781806349754333, "logps/chosen": -321.5018005371094, "logps/rejected": -276.88409423828125, "logits/chosen": -1.4394444227218628, "logits/rejected": -1.4822733402252197, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1236329078674316, "stability/repetition_rate_mean": 0.7289062738418579}
{"current_steps": 300, "total_steps": 927, "eval_loss": 0.513998806476593, "epoch": 0.32384293617595467, "percentage": 32.36, "elapsed_time": "1:17:19", "remaining_time": "2:41:35"}
{"current_steps": 310, "total_steps": 927, "loss": 0.534, "accuracy": 0.75, "lr": 4.217125683458161e-07, "epoch": 0.33463770071515314, "percentage": 33.44, "elapsed_time": "1:19:45", "remaining_time": "2:38:45", "rewards/chosen": 0.6554938554763794, "rewards/rejected": 0.0268485676497221, "rewards/accuracies": 0.75, "rewards/margins": 0.6286452412605286, "logps/chosen": -315.331298828125, "logps/rejected": -258.9646911621094, "logits/chosen": -1.4233049154281616, "logits/rejected": -1.452370285987854, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.908203125, "stability/repetition_rate_mean": 0.799023449420929}
{"current_steps": 320, "total_steps": 927, "loss": 0.5117, "accuracy": 0.760937511920929, "lr": 4.1474793226723825e-07, "epoch": 0.3454324652543516, "percentage": 34.52, "elapsed_time": "1:22:15", "remaining_time": "2:36:02", "rewards/chosen": 0.7362013459205627, "rewards/rejected": 0.048347149044275284, "rewards/accuracies": 0.760937511920929, "rewards/margins": 0.6878542304039001, "logps/chosen": -327.7336120605469, "logps/rejected": -262.0153503417969, "logits/chosen": -1.448975682258606, "logits/rejected": -1.4607841968536377, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.083886742591858, "stability/repetition_rate_mean": 0.9072265625}
{"current_steps": 330, "total_steps": 927, "loss": 0.4903, "accuracy": 0.7562500238418579, "lr": 4.0754955445415396e-07, "epoch": 0.35622722979355015, "percentage": 35.6, "elapsed_time": "1:24:37", "remaining_time": "2:33:05", "rewards/chosen": 0.7997223734855652, "rewards/rejected": 0.012823845259845257, "rewards/accuracies": 0.7562500238418579, "rewards/margins": 0.7868985533714294, "logps/chosen": -330.9326477050781, "logps/rejected": -256.48638916015625, "logits/chosen": -1.447295069694519, "logits/rejected": -1.47311270236969, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7205078601837158, "stability/repetition_rate_mean": 0.955273449420929}
{"current_steps": 340, "total_steps": 927, "loss": 0.4981, "accuracy": 0.770312488079071, "lr": 4.001276478500126e-07, "epoch": 0.3670219943327486, "percentage": 36.68, "elapsed_time": "1:27:04", "remaining_time": "2:30:19", "rewards/chosen": 0.7650503516197205, "rewards/rejected": 0.026440534740686417, "rewards/accuracies": 0.770312488079071, "rewards/margins": 0.738609790802002, "logps/chosen": -316.4836120605469, "logps/rejected": -258.7716064453125, "logits/chosen": -1.4461034536361694, "logits/rejected": -1.4699530601501465, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.8572266101837158, "stability/repetition_rate_mean": 0.7232421636581421}
{"current_steps": 350, "total_steps": 927, "loss": 0.5247, "accuracy": 0.7359375357627869, "lr": 3.9249274253734164e-07, "epoch": 0.3778167588719471, "percentage": 37.76, "elapsed_time": "1:29:29", "remaining_time": "2:27:32", "rewards/chosen": 0.7249447107315063, "rewards/rejected": 0.012191593647003174, "rewards/accuracies": 0.7359375357627869, "rewards/margins": 0.712753176689148, "logps/chosen": -314.6985778808594, "logps/rejected": -269.089599609375, "logits/chosen": -1.4219696521759033, "logits/rejected": -1.4259979724884033, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2517578601837158, "stability/repetition_rate_mean": 0.76953125}
{"current_steps": 360, "total_steps": 927, "loss": 0.5044, "accuracy": 0.7515625357627869, "lr": 3.846556707978337e-07, "epoch": 0.3886115234111456, "percentage": 38.83, "elapsed_time": "1:31:53", "remaining_time": "2:24:43", "rewards/chosen": 0.8103561401367188, "rewards/rejected": 0.039548713713884354, "rewards/accuracies": 0.7515625357627869, "rewards/margins": 0.7708074450492859, "logps/chosen": -334.6982727050781, "logps/rejected": -252.6654815673828, "logits/chosen": -1.427175760269165, "logits/rejected": -1.461297869682312, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.2664062976837158, "stability/repetition_rate_mean": 0.889843761920929}
{"current_steps": 370, "total_steps": 927, "loss": 0.4983, "accuracy": 0.729687511920929, "lr": 3.766275517436779e-07, "epoch": 0.3994062879503441, "percentage": 39.91, "elapsed_time": "1:34:19", "remaining_time": "2:21:59", "rewards/chosen": 0.7894806861877441, "rewards/rejected": 0.03071962669491768, "rewards/accuracies": 0.729687511920929, "rewards/margins": 0.7587611079216003, "logps/chosen": -319.5567321777344, "logps/rejected": -254.91746520996094, "logits/chosen": -1.3873792886734009, "logits/rejected": -1.3985546827316284, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.752539038658142, "stability/repetition_rate_mean": 0.777148425579071}
{"current_steps": 380, "total_steps": 927, "loss": 0.5001, "accuracy": 0.7593750357627869, "lr": 3.684197755419419e-07, "epoch": 0.4102010524895426, "percentage": 40.99, "elapsed_time": "1:36:43", "remaining_time": "2:19:14", "rewards/chosen": 0.7916366457939148, "rewards/rejected": 0.01186272781342268, "rewards/accuracies": 0.7593750357627869, "rewards/margins": 0.7797738909721375, "logps/chosen": -306.7962341308594, "logps/rejected": -258.84234619140625, "logits/chosen": -1.4125322103500366, "logits/rejected": -1.417395830154419, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.844921827316284, "stability/repetition_rate_mean": 0.7945312261581421}
{"current_steps": 390, "total_steps": 927, "loss": 0.4952, "accuracy": 0.765625, "lr": 3.60043987254384e-07, "epoch": 0.42099581702874106, "percentage": 42.07, "elapsed_time": "1:39:06", "remaining_time": "2:16:27", "rewards/chosen": 0.8086903691291809, "rewards/rejected": -0.04293971136212349, "rewards/accuracies": 0.765625, "rewards/margins": 0.8516300320625305, "logps/chosen": -316.85101318359375, "logps/rejected": -264.2350769042969, "logits/chosen": -1.425724983215332, "logits/rejected": -1.4372109174728394, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.869531273841858, "stability/repetition_rate_mean": 0.74609375}
{"current_steps": 400, "total_steps": 927, "loss": 0.5137, "accuracy": 0.776562511920929, "lr": 3.5151207031562633e-07, "epoch": 0.43179058156793954, "percentage": 43.15, "elapsed_time": "1:41:29", "remaining_time": "2:13:43", "rewards/chosen": 0.7608602046966553, "rewards/rejected": -0.023676123470067978, "rewards/accuracies": 0.776562511920929, "rewards/margins": 0.7845363020896912, "logps/chosen": -310.2779235839844, "logps/rejected": -268.259033203125, "logits/chosen": -1.3795498609542847, "logits/rejected": -1.3812373876571655, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6525390148162842, "stability/repetition_rate_mean": 0.7679687738418579}
{"current_steps": 410, "total_steps": 927, "loss": 0.5069, "accuracy": 0.760937511920929, "lr": 3.4283612967312687e-07, "epoch": 0.442585346107138, "percentage": 44.23, "elapsed_time": "1:43:55", "remaining_time": "2:11:03", "rewards/chosen": 0.7829875349998474, "rewards/rejected": -0.021388515830039978, "rewards/accuracies": 0.760937511920929, "rewards/margins": 0.8043760657310486, "logps/chosen": -313.1993103027344, "logps/rejected": -250.6515655517578, "logits/chosen": -1.3496493101119995, "logits/rejected": -1.3831802606582642, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6925780773162842, "stability/repetition_rate_mean": 0.854296863079071}
{"current_steps": 420, "total_steps": 927, "loss": 0.5122, "accuracy": 0.737500011920929, "lr": 3.34028474612874e-07, "epoch": 0.45338011064633654, "percentage": 45.31, "elapsed_time": "1:46:17", "remaining_time": "2:08:18", "rewards/chosen": 0.7716613411903381, "rewards/rejected": -0.07191526889801025, "rewards/accuracies": 0.737500011920929, "rewards/margins": 0.8435766100883484, "logps/chosen": -299.86065673828125, "logps/rejected": -261.5897521972656, "logits/chosen": -1.3803904056549072, "logits/rejected": -1.394149661064148, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1460938453674316, "stability/repetition_rate_mean": 0.822460949420929}
{"current_steps": 430, "total_steps": 927, "loss": 0.4937, "accuracy": 0.768750011920929, "lr": 3.2510160129516775e-07, "epoch": 0.464174875185535, "percentage": 46.39, "elapsed_time": "1:48:35", "remaining_time": "2:05:30", "rewards/chosen": 0.8203991055488586, "rewards/rejected": -0.07847942411899567, "rewards/accuracies": 0.768750011920929, "rewards/margins": 0.8988785147666931, "logps/chosen": -319.5307922363281, "logps/rejected": -265.67938232421875, "logits/chosen": -1.3800678253173828, "logits/rejected": -1.3930643796920776, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.808789014816284, "stability/repetition_rate_mean": 0.78125}
{"current_steps": 440, "total_steps": 927, "loss": 0.4891, "accuracy": 0.792187511920929, "lr": 3.1606817502526736e-07, "epoch": 0.4749696397247335, "percentage": 47.46, "elapsed_time": "1:50:53", "remaining_time": "2:02:44", "rewards/chosen": 0.8151880502700806, "rewards/rejected": -0.05617132410407066, "rewards/accuracies": 0.792187511920929, "rewards/margins": 0.8713592886924744, "logps/chosen": -326.2301330566406, "logps/rejected": -256.16412353515625, "logits/chosen": -1.3767389059066772, "logits/rejected": -1.4001781940460205, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.164990186691284, "stability/repetition_rate_mean": 0.8707031011581421}
{"current_steps": 450, "total_steps": 927, "loss": 0.4817, "accuracy": 0.7890625, "lr": 3.069410122840585e-07, "epoch": 0.48576440426393197, "percentage": 48.54, "elapsed_time": "1:53:16", "remaining_time": "2:00:04", "rewards/chosen": 0.8885055780410767, "rewards/rejected": -0.03621084615588188, "rewards/accuracies": 0.7890625, "rewards/margins": 0.924716591835022, "logps/chosen": -318.3290100097656, "logps/rejected": -268.3873596191406, "logits/chosen": -1.372667670249939, "logits/rejected": -1.4003467559814453, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7429687976837158, "stability/repetition_rate_mean": 0.8236328363418579}
{"current_steps": 460, "total_steps": 927, "loss": 0.4832, "accuracy": 0.7718750238418579, "lr": 2.9773306254423513e-07, "epoch": 0.4965591688031305, "percentage": 49.62, "elapsed_time": "1:55:38", "remaining_time": "1:57:23", "rewards/chosen": 0.853641152381897, "rewards/rejected": -0.016610044986009598, "rewards/accuracies": 0.7718750238418579, "rewards/margins": 0.8702511191368103, "logps/chosen": -294.7362976074219, "logps/rejected": -240.1558074951172, "logits/chosen": -1.3681141138076782, "logits/rejected": -1.3875712156295776, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7371094226837158, "stability/repetition_rate_mean": 0.8646484613418579}
{"current_steps": 470, "total_steps": 927, "loss": 0.4801, "accuracy": 0.7593750357627869, "lr": 2.884573898977941e-07, "epoch": 0.5073539333423289, "percentage": 50.7, "elapsed_time": "1:58:03", "remaining_time": "1:54:47", "rewards/chosen": 0.8505555391311646, "rewards/rejected": -0.04154070094227791, "rewards/accuracies": 0.7593750357627869, "rewards/margins": 0.8920961618423462, "logps/chosen": -308.7089538574219, "logps/rejected": -268.0931091308594, "logits/chosen": -1.388026237487793, "logits/rejected": -1.3998411893844604, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.244531273841858, "stability/repetition_rate_mean": 0.8687499761581421}
{"current_steps": 480, "total_steps": 927, "loss": 0.4827, "accuracy": 0.7890625, "lr": 2.791271545209101e-07, "epoch": 0.5181486978815275, "percentage": 51.78, "elapsed_time": "2:00:30", "remaining_time": "1:52:13", "rewards/chosen": 0.8823606371879578, "rewards/rejected": 0.021032243967056274, "rewards/accuracies": 0.7890625, "rewards/margins": 0.8613284230232239, "logps/chosen": -294.66412353515625, "logps/rejected": -248.6920928955078, "logits/chosen": -1.3824865818023682, "logits/rejected": -1.4162545204162598, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1624999046325684, "stability/repetition_rate_mean": 0.8519531488418579}
{"current_steps": 490, "total_steps": 927, "loss": 0.4647, "accuracy": 0.792187511920929, "lr": 2.697555940024887e-07, "epoch": 0.528943462420726, "percentage": 52.86, "elapsed_time": "2:02:55", "remaining_time": "1:49:37", "rewards/chosen": 0.9351348280906677, "rewards/rejected": -0.035314515233039856, "rewards/accuracies": 0.792187511920929, "rewards/margins": 0.9704492688179016, "logps/chosen": -303.913330078125, "logps/rejected": -242.33706665039062, "logits/chosen": -1.3622970581054688, "logits/rejected": -1.3937467336654663, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9038817882537842, "stability/repetition_rate_mean": 0.8125}
{"current_steps": 500, "total_steps": 927, "loss": 0.482, "accuracy": 0.7828125357627869, "lr": 2.603560045628857e-07, "epoch": 0.5397382269599245, "percentage": 53.94, "elapsed_time": "2:05:22", "remaining_time": "1:47:03", "rewards/chosen": 0.9495781064033508, "rewards/rejected": -0.016795417293906212, "rewards/accuracies": 0.7828125357627869, "rewards/margins": 0.9663735628128052, "logps/chosen": -314.1101989746094, "logps/rejected": -261.1115417480469, "logits/chosen": -1.3485071659088135, "logits/rejected": -1.3485997915267944, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1546874046325684, "stability/repetition_rate_mean": 0.7855468988418579}
{"current_steps": 510, "total_steps": 927, "loss": 0.4861, "accuracy": 0.796875, "lr": 2.509417221894427e-07, "epoch": 0.5505329914991229, "percentage": 55.02, "elapsed_time": "2:07:46", "remaining_time": "1:44:28", "rewards/chosen": 0.9477736353874207, "rewards/rejected": 0.007596464361995459, "rewards/accuracies": 0.796875, "rewards/margins": 0.9401771426200867, "logps/chosen": -307.50494384765625, "logps/rejected": -269.51959228515625, "logits/chosen": -1.3849689960479736, "logits/rejected": -1.3808684349060059, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.330175757408142, "stability/repetition_rate_mean": 0.8619140386581421}
{"current_steps": 520, "total_steps": 927, "loss": 0.4896, "accuracy": 0.765625, "lr": 2.4152610371560093e-07, "epoch": 0.5613277560383214, "percentage": 56.09, "elapsed_time": "2:10:12", "remaining_time": "1:41:55", "rewards/chosen": 0.9386131167411804, "rewards/rejected": 0.06416996568441391, "rewards/accuracies": 0.765625, "rewards/margins": 0.8744432330131531, "logps/chosen": -308.14276123046875, "logps/rejected": -257.72283935546875, "logits/chosen": -1.4082396030426025, "logits/rejected": -1.4073408842086792, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.715429663658142, "stability/repetition_rate_mean": 0.856640636920929}
{"current_steps": 530, "total_steps": 927, "loss": 0.4874, "accuracy": 0.7562500238418579, "lr": 2.321225078704399e-07, "epoch": 0.5721225205775199, "percentage": 57.17, "elapsed_time": "2:12:36", "remaining_time": "1:39:19", "rewards/chosen": 1.0152686834335327, "rewards/rejected": 0.1395503580570221, "rewards/accuracies": 0.7562500238418579, "rewards/margins": 0.8757182955741882, "logps/chosen": -317.8136291503906, "logps/rejected": -260.39678955078125, "logits/chosen": -1.4284799098968506, "logits/rejected": -1.4444469213485718, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.365820288658142, "stability/repetition_rate_mean": 0.7701171636581421}
{"current_steps": 540, "total_steps": 927, "loss": 0.4931, "accuracy": 0.7734375, "lr": 2.2274427632552503e-07, "epoch": 0.5829172851167184, "percentage": 58.25, "elapsed_time": "2:14:57", "remaining_time": "1:36:43", "rewards/chosen": 0.9857921600341797, "rewards/rejected": 0.1031401976943016, "rewards/accuracies": 0.7734375, "rewards/margins": 0.8826519250869751, "logps/chosen": -302.94537353515625, "logps/rejected": -262.5538635253906, "logits/chosen": -1.4013619422912598, "logits/rejected": -1.405015468597412, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7644531726837158, "stability/repetition_rate_mean": 0.8062499761581421}
{"current_steps": 550, "total_steps": 927, "loss": 0.4744, "accuracy": 0.7671875357627869, "lr": 2.134047147659583e-07, "epoch": 0.5937120496559168, "percentage": 59.33, "elapsed_time": "2:17:23", "remaining_time": "1:34:10", "rewards/chosen": 1.062072992324829, "rewards/rejected": 0.140531525015831, "rewards/accuracies": 0.7671875357627869, "rewards/margins": 0.9215413928031921, "logps/chosen": -309.70013427734375, "logps/rejected": -264.7682189941406, "logits/chosen": -1.3965346813201904, "logits/rejected": -1.3893383741378784, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9962890148162842, "stability/repetition_rate_mean": 0.8472656011581421}
{"current_steps": 560, "total_steps": 927, "loss": 0.5088, "accuracy": 0.7328125238418579, "lr": 2.0411707401248403e-07, "epoch": 0.6045068141951153, "percentage": 60.41, "elapsed_time": "2:19:50", "remaining_time": "1:31:38", "rewards/chosen": 1.0473458766937256, "rewards/rejected": 0.2058195173740387, "rewards/accuracies": 0.7328125238418579, "rewards/margins": 0.841526210308075, "logps/chosen": -310.02789306640625, "logps/rejected": -263.0741882324219, "logits/chosen": -1.4111377000808716, "logits/rejected": -1.4183025360107422, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.4849610328674316, "stability/repetition_rate_mean": 0.784960925579071}
{"current_steps": 570, "total_steps": 927, "loss": 0.4437, "accuracy": 0.793749988079071, "lr": 1.9489453122143603e-07, "epoch": 0.6153015787343139, "percentage": 61.49, "elapsed_time": "2:22:14", "remaining_time": "1:29:05", "rewards/chosen": 1.1061866283416748, "rewards/rejected": 0.11313938349485397, "rewards/accuracies": 0.793749988079071, "rewards/margins": 0.9930472373962402, "logps/chosen": -312.1006164550781, "logps/rejected": -254.21788024902344, "logits/chosen": -1.4051156044006348, "logits/rejected": -1.4276247024536133, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.817578136920929, "stability/repetition_rate_mean": 0.828906238079071}
{"current_steps": 580, "total_steps": 927, "loss": 0.5064, "accuracy": 0.746874988079071, "lr": 1.8575017118919928e-07, "epoch": 0.6260963432735124, "percentage": 62.57, "elapsed_time": "2:24:44", "remaining_time": "1:26:35", "rewards/chosen": 0.9926168322563171, "rewards/rejected": 0.16463755071163177, "rewards/accuracies": 0.746874988079071, "rewards/margins": 0.8279793858528137, "logps/chosen": -319.3863830566406, "logps/rejected": -252.8662109375, "logits/chosen": -1.4090861082077026, "logits/rejected": -1.4279987812042236, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6736328601837158, "stability/repetition_rate_mean": 0.7662109136581421}
{"current_steps": 590, "total_steps": 927, "loss": 0.4494, "accuracy": 0.800000011920929, "lr": 1.7669696778770938e-07, "epoch": 0.6368911078127109, "percentage": 63.65, "elapsed_time": "2:27:04", "remaining_time": "1:24:00", "rewards/chosen": 1.0922644138336182, "rewards/rejected": 0.08526992052793503, "rewards/accuracies": 0.800000011920929, "rewards/margins": 1.006994605064392, "logps/chosen": -316.9762268066406, "logps/rejected": -245.96548461914062, "logits/chosen": -1.4007326364517212, "logits/rejected": -1.4263814687728882, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.351171851158142, "stability/repetition_rate_mean": 0.8466796875}
{"current_steps": 600, "total_steps": 927, "loss": 0.479, "accuracy": 0.768750011920929, "lr": 1.6774776555733028e-07, "epoch": 0.6476858723519093, "percentage": 64.72, "elapsed_time": "2:29:29", "remaining_time": "1:21:28", "rewards/chosen": 1.0857884883880615, "rewards/rejected": 0.14797575771808624, "rewards/accuracies": 0.768750011920929, "rewards/margins": 0.9378126263618469, "logps/chosen": -317.29132080078125, "logps/rejected": -270.30157470703125, "logits/chosen": -1.382874846458435, "logits/rejected": -1.3788057565689087, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.7417969703674316, "stability/repetition_rate_mean": 0.8042968511581421}
{"current_steps": 600, "total_steps": 927, "eval_loss": 0.47359293699264526, "epoch": 0.6476858723519093, "percentage": 64.72, "elapsed_time": "2:34:01", "remaining_time": "1:23:56"}
{"current_steps": 610, "total_steps": 927, "loss": 0.4827, "accuracy": 0.785937488079071, "lr": 1.5891526148322593e-07, "epoch": 0.6584806368911078, "percentage": 65.8, "elapsed_time": "2:36:28", "remaining_time": "1:21:18", "rewards/chosen": 1.0058691501617432, "rewards/rejected": 0.11844401806592941, "rewards/accuracies": 0.785937488079071, "rewards/margins": 0.8874250650405884, "logps/chosen": -337.97906494140625, "logps/rejected": -273.7126159667969, "logits/chosen": -1.3868850469589233, "logits/rejected": -1.386324167251587, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.9982421398162842, "stability/repetition_rate_mean": 0.744921863079071}
{"current_steps": 620, "total_steps": 927, "loss": 0.494, "accuracy": 0.7734375, "lr": 1.5021198698108036e-07, "epoch": 0.6692754014303063, "percentage": 66.88, "elapsed_time": "2:38:51", "remaining_time": "1:18:39", "rewards/chosen": 1.0009812116622925, "rewards/rejected": 0.0862690731883049, "rewards/accuracies": 0.7734375, "rewards/margins": 0.914712131023407, "logps/chosen": -317.61688232421875, "logps/rejected": -255.9473114013672, "logits/chosen": -1.37397038936615, "logits/rejected": -1.378446102142334, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.2964844703674316, "stability/repetition_rate_mean": 0.8783203363418579}
{"current_steps": 630, "total_steps": 927, "loss": 0.4832, "accuracy": 0.7640625238418579, "lr": 1.416502901177251e-07, "epoch": 0.6800701659695048, "percentage": 67.96, "elapsed_time": "2:41:13", "remaining_time": "1:16:00", "rewards/chosen": 0.9517079591751099, "rewards/rejected": 0.09470874816179276, "rewards/accuracies": 0.7640625238418579, "rewards/margins": 0.8569992184638977, "logps/chosen": -311.6447448730469, "logps/rejected": -254.44578552246094, "logits/chosen": -1.3341145515441895, "logits/rejected": -1.3377490043640137, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.377539038658142, "stability/repetition_rate_mean": 0.8720703125}
{"current_steps": 640, "total_steps": 927, "loss": 0.4615, "accuracy": 0.800000011920929, "lr": 1.3324231809189983e-07, "epoch": 0.6908649305087032, "percentage": 69.04, "elapsed_time": "2:43:35", "remaining_time": "1:13:21", "rewards/chosen": 1.0257498025894165, "rewards/rejected": -0.0014841229422017932, "rewards/accuracies": 0.800000011920929, "rewards/margins": 1.0272339582443237, "logps/chosen": -319.6551818847656, "logps/rejected": -258.7291564941406, "logits/chosen": -1.3613817691802979, "logits/rejected": -1.3694230318069458, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7732422351837158, "stability/repetition_rate_mean": 0.7818359136581421}
{"current_steps": 650, "total_steps": 927, "loss": 0.4443, "accuracy": 0.800000011920929, "lr": 1.2500000000000005e-07, "epoch": 0.7016596950479018, "percentage": 70.12, "elapsed_time": "2:45:50", "remaining_time": "1:10:40", "rewards/chosen": 0.9646196365356445, "rewards/rejected": -0.04443943500518799, "rewards/accuracies": 0.800000011920929, "rewards/margins": 1.0090590715408325, "logps/chosen": -292.4341735839844, "logps/rejected": -252.3300323486328, "logits/chosen": -1.322688341140747, "logits/rejected": -1.3375654220581055, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.5888671875, "stability/repetition_rate_mean": 0.850390613079071}
{"current_steps": 660, "total_steps": 927, "loss": 0.461, "accuracy": 0.7890625, "lr": 1.1693502991126608e-07, "epoch": 0.7124544595871003, "percentage": 71.2, "elapsed_time": "2:48:08", "remaining_time": "1:08:01", "rewards/chosen": 1.0033434629440308, "rewards/rejected": -0.03249470144510269, "rewards/accuracies": 0.7890625, "rewards/margins": 1.0358381271362305, "logps/chosen": -333.7082214355469, "logps/rejected": -250.5390625, "logits/chosen": -1.3551677465438843, "logits/rejected": -1.361363410949707, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.3021483421325684, "stability/repetition_rate_mean": 0.775585949420929}
{"current_steps": 670, "total_steps": 927, "loss": 0.477, "accuracy": 0.78125, "lr": 1.0905885027642483e-07, "epoch": 0.7232492241262988, "percentage": 72.28, "elapsed_time": "2:50:29", "remaining_time": "1:05:23", "rewards/chosen": 0.9816705584526062, "rewards/rejected": -0.0048652635887265205, "rewards/accuracies": 0.78125, "rewards/margins": 0.9865358471870422, "logps/chosen": -295.5746765136719, "logps/rejected": -237.2896728515625, "logits/chosen": -1.3491506576538086, "logits/rejected": -1.3566621541976929, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.6103515625, "stability/repetition_rate_mean": 0.785937488079071}
{"current_steps": 680, "total_steps": 927, "loss": 0.4736, "accuracy": 0.7796875238418579, "lr": 1.0138263569332267e-07, "epoch": 0.7340439886654972, "percentage": 73.35, "elapsed_time": "2:52:53", "remaining_time": "1:02:47", "rewards/chosen": 1.0467315912246704, "rewards/rejected": 0.059045251458883286, "rewards/accuracies": 0.7796875238418579, "rewards/margins": 0.9876863360404968, "logps/chosen": -314.92364501953125, "logps/rejected": -262.690673828125, "logits/chosen": -1.372681975364685, "logits/rejected": -1.3638715744018555, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3708984851837158, "stability/repetition_rate_mean": 0.7417968511581421}
{"current_steps": 690, "total_steps": 927, "loss": 0.4613, "accuracy": 0.785937488079071, "lr": 9.391727705258502e-08, "epoch": 0.7448387532046957, "percentage": 74.43, "elapsed_time": "2:55:18", "remaining_time": "1:00:12", "rewards/chosen": 1.0267409086227417, "rewards/rejected": -0.01362178660929203, "rewards/accuracies": 0.785937488079071, "rewards/margins": 1.0403625965118408, "logps/chosen": -320.1853942871094, "logps/rejected": -271.3731384277344, "logits/chosen": -1.3565599918365479, "logits/rejected": -1.3600114583969116, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.596289038658142, "stability/repetition_rate_mean": 0.8603515625}
{"current_steps": 700, "total_steps": 927, "loss": 0.4775, "accuracy": 0.801562488079071, "lr": 8.667336608579487e-08, "epoch": 0.7556335177438942, "percentage": 75.51, "elapsed_time": "2:57:46", "remaining_time": "0:57:38", "rewards/chosen": 0.9467880129814148, "rewards/rejected": 0.011792674660682678, "rewards/accuracies": 0.801562488079071, "rewards/margins": 0.9349954724311829, "logps/chosen": -318.6557312011719, "logps/rejected": -261.914794921875, "logits/chosen": -1.3605684041976929, "logits/rejected": -1.3892539739608765, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.692675828933716, "stability/repetition_rate_mean": 0.8111327886581421}
{"current_steps": 710, "total_steps": 927, "loss": 0.4735, "accuracy": 0.796875, "lr": 7.96611803381127e-08, "epoch": 0.7664282822830927, "percentage": 76.59, "elapsed_time": "3:00:10", "remaining_time": "0:55:04", "rewards/chosen": 0.9548945426940918, "rewards/rejected": -0.02534562349319458, "rewards/accuracies": 0.796875, "rewards/margins": 0.9802401661872864, "logps/chosen": -303.3160095214844, "logps/rejected": -258.77264404296875, "logits/chosen": -1.3547624349594116, "logits/rejected": -1.3488959074020386, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.209765672683716, "stability/repetition_rate_mean": 0.840039074420929}
{"current_steps": 720, "total_steps": 927, "loss": 0.4632, "accuracy": 0.770312488079071, "lr": 7.28906685866599e-08, "epoch": 0.7772230468222912, "percentage": 77.67, "elapsed_time": "3:02:37", "remaining_time": "0:52:30", "rewards/chosen": 1.0151904821395874, "rewards/rejected": -0.02825441025197506, "rewards/accuracies": 0.770312488079071, "rewards/margins": 1.0434449911117554, "logps/chosen": -314.6918640136719, "logps/rejected": -246.10391235351562, "logits/chosen": -1.3702967166900635, "logits/rejected": -1.3795828819274902, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 0.8656250238418579, "stability/repetition_rate_mean": 0.8203125}
{"current_steps": 730, "total_steps": 927, "loss": 0.4905, "accuracy": 0.78125, "lr": 6.637143672535281e-08, "epoch": 0.7880178113614896, "percentage": 78.75, "elapsed_time": "3:05:00", "remaining_time": "0:49:55", "rewards/chosen": 1.0083658695220947, "rewards/rejected": 0.06377997249364853, "rewards/accuracies": 0.78125, "rewards/margins": 0.9445858001708984, "logps/chosen": -312.05279541015625, "logps/rejected": -256.790283203125, "logits/chosen": -1.3211309909820557, "logits/rejected": -1.3452644348144531, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.055468797683716, "stability/repetition_rate_mean": 0.7437499761581421}
{"current_steps": 740, "total_steps": 927, "loss": 0.4786, "accuracy": 0.7828125357627869, "lr": 6.01127341362138e-08, "epoch": 0.7988125759006882, "percentage": 79.83, "elapsed_time": "3:07:28", "remaining_time": "0:47:22", "rewards/chosen": 0.9195186495780945, "rewards/rejected": -0.05838441848754883, "rewards/accuracies": 0.7828125357627869, "rewards/margins": 0.9779030680656433, "logps/chosen": -314.6363220214844, "logps/rejected": -265.0621032714844, "logits/chosen": -1.339066505432129, "logits/rejected": -1.343850016593933, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.513671875, "stability/repetition_rate_mean": 0.775195300579071}
{"current_steps": 750, "total_steps": 927, "loss": 0.4702, "accuracy": 0.78125, "lr": 5.412344056649526e-08, "epoch": 0.8096073404398867, "percentage": 80.91, "elapsed_time": "3:09:52", "remaining_time": "0:44:48", "rewards/chosen": 1.004286766052246, "rewards/rejected": -0.05221066623926163, "rewards/accuracies": 0.78125, "rewards/margins": 1.0564974546432495, "logps/chosen": -323.9461364746094, "logps/rejected": -263.8832702636719, "logits/chosen": -1.3286861181259155, "logits/rejected": -1.3161166906356812, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.7333984375, "stability/repetition_rate_mean": 0.825390636920929}
{"current_steps": 760, "total_steps": 927, "loss": 0.4974, "accuracy": 0.7718750238418579, "lr": 4.841205353023714e-08, "epoch": 0.8204021049790852, "percentage": 81.98, "elapsed_time": "3:12:18", "remaining_time": "0:42:15", "rewards/chosen": 0.985223114490509, "rewards/rejected": 0.0688353106379509, "rewards/accuracies": 0.7718750238418579, "rewards/margins": 0.9163877367973328, "logps/chosen": -308.4322814941406, "logps/rejected": -251.6645965576172, "logits/chosen": -1.316092848777771, "logits/rejected": -1.3103103637695312, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.017578125, "stability/repetition_rate_mean": 0.779101550579071}
{"current_steps": 770, "total_steps": 927, "loss": 0.4839, "accuracy": 0.776562511920929, "lr": 4.298667625212904e-08, "epoch": 0.8311968695182836, "percentage": 83.06, "elapsed_time": "3:14:44", "remaining_time": "0:39:42", "rewards/chosen": 0.9914433360099792, "rewards/rejected": 0.01984873227775097, "rewards/accuracies": 0.776562511920929, "rewards/margins": 0.9715946316719055, "logps/chosen": -312.3675842285156, "logps/rejected": -260.13189697265625, "logits/chosen": -1.3329784870147705, "logits/rejected": -1.3454676866531372, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.833984375, "stability/repetition_rate_mean": 0.8814452886581421}
{"current_steps": 780, "total_steps": 927, "loss": 0.451, "accuracy": 0.8109375238418579, "lr": 3.785500617078424e-08, "epoch": 0.8419916340574821, "percentage": 84.14, "elapsed_time": "3:17:10", "remaining_time": "0:37:09", "rewards/chosen": 1.0986813306808472, "rewards/rejected": -0.03042042814195156, "rewards/accuracies": 0.8109375238418579, "rewards/margins": 1.1291017532348633, "logps/chosen": -339.4462585449219, "logps/rejected": -283.0810546875, "logits/chosen": -1.3594963550567627, "logits/rejected": -1.3589779138565063, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.575585961341858, "stability/repetition_rate_mean": 0.795703113079071}
{"current_steps": 790, "total_steps": 927, "loss": 0.4454, "accuracy": 0.809374988079071, "lr": 3.3024324017737554e-08, "epoch": 0.8527863985966806, "percentage": 85.22, "elapsed_time": "3:19:37", "remaining_time": "0:34:37", "rewards/chosen": 1.0409488677978516, "rewards/rejected": -0.016527360305190086, "rewards/accuracies": 0.809374988079071, "rewards/margins": 1.057476282119751, "logps/chosen": -314.4818115234375, "logps/rejected": -266.3319396972656, "logits/chosen": -1.341722846031189, "logits/rejected": -1.332808256149292, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.439843773841858, "stability/repetition_rate_mean": 0.8095703125}
{"current_steps": 800, "total_steps": 927, "loss": 0.4918, "accuracy": 0.770312488079071, "lr": 2.850148348765921e-08, "epoch": 0.8635811631358791, "percentage": 86.3, "elapsed_time": "3:22:00", "remaining_time": "0:32:04", "rewards/chosen": 0.9924200177192688, "rewards/rejected": 0.09035740047693253, "rewards/accuracies": 0.770312488079071, "rewards/margins": 0.9020625948905945, "logps/chosen": -304.0136413574219, "logps/rejected": -258.58209228515625, "logits/chosen": -1.3520551919937134, "logits/rejected": -1.355186939239502, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.61376953125, "stability/repetition_rate_mean": 0.9134765863418579}
{"current_steps": 810, "total_steps": 927, "loss": 0.4636, "accuracy": 0.7875000238418579, "lr": 2.4292901514442327e-08, "epoch": 0.8743759276750775, "percentage": 87.38, "elapsed_time": "3:24:24", "remaining_time": "0:29:31", "rewards/chosen": 1.0107624530792236, "rewards/rejected": 0.011880074627697468, "rewards/accuracies": 0.7875000238418579, "rewards/margins": 0.9988824725151062, "logps/chosen": -321.5726013183594, "logps/rejected": -258.95526123046875, "logits/chosen": -1.3454469442367554, "logits/rejected": -1.3386812210083008, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4040038585662842, "stability/repetition_rate_mean": 0.8667968511581421}
{"current_steps": 820, "total_steps": 927, "loss": 0.4683, "accuracy": 0.7796875238418579, "lr": 2.0404549166959718e-08, "epoch": 0.885170692214276, "percentage": 88.46, "elapsed_time": "3:26:51", "remaining_time": "0:26:59", "rewards/chosen": 1.0170072317123413, "rewards/rejected": 0.03334783762693405, "rewards/accuracies": 0.7796875238418579, "rewards/margins": 0.9836593866348267, "logps/chosen": -308.5863037109375, "logps/rejected": -254.77041625976562, "logits/chosen": -1.300979495048523, "logits/rejected": -1.3161647319793701, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.8826172351837158, "stability/repetition_rate_mean": 0.9224609136581421}
{"current_steps": 830, "total_steps": 927, "loss": 0.4925, "accuracy": 0.7828125357627869, "lr": 1.6841943177406976e-08, "epoch": 0.8959654567534746, "percentage": 89.54, "elapsed_time": "3:29:17", "remaining_time": "0:24:27", "rewards/chosen": 0.9902805685997009, "rewards/rejected": -0.030288374051451683, "rewards/accuracies": 0.7828125357627869, "rewards/margins": 1.020569086074829, "logps/chosen": -319.1125793457031, "logps/rejected": -274.5699462890625, "logits/chosen": -1.3147996664047241, "logits/rejected": -1.3255729675292969, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.1563477516174316, "stability/repetition_rate_mean": 0.8330078125}
{"current_steps": 840, "total_steps": 927, "loss": 0.4735, "accuracy": 0.765625, "lr": 1.3610138114250519e-08, "epoch": 0.9067602212926731, "percentage": 90.61, "elapsed_time": "3:31:45", "remaining_time": "0:21:55", "rewards/chosen": 1.0660076141357422, "rewards/rejected": 0.06570061296224594, "rewards/accuracies": 0.765625, "rewards/margins": 1.0003070831298828, "logps/chosen": -327.689697265625, "logps/rejected": -261.86480712890625, "logits/chosen": -1.327235221862793, "logits/rejected": -1.343729853630066, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4929687976837158, "stability/repetition_rate_mean": 0.8158203363418579}
{"current_steps": 850, "total_steps": 927, "loss": 0.4859, "accuracy": 0.776562511920929, "lr": 1.0713719210886928e-08, "epoch": 0.9175549858318716, "percentage": 91.69, "elapsed_time": "3:34:07", "remaining_time": "0:19:23", "rewards/chosen": 1.053748369216919, "rewards/rejected": 0.05971544608473778, "rewards/accuracies": 0.776562511920929, "rewards/margins": 0.9940328598022461, "logps/chosen": -324.7861328125, "logps/rejected": -261.70849609375, "logits/chosen": -1.3229340314865112, "logits/rejected": -1.3207443952560425, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3533203601837158, "stability/repetition_rate_mean": 0.7544921636581421}
{"current_steps": 860, "total_steps": 927, "loss": 0.4306, "accuracy": 0.8109375238418579, "lr": 8.156795860187027e-09, "epoch": 0.92834975037107, "percentage": 92.77, "elapsed_time": "3:36:34", "remaining_time": "0:16:52", "rewards/chosen": 1.0677509307861328, "rewards/rejected": -0.06025683507323265, "rewards/accuracies": 0.8109375238418579, "rewards/margins": 1.1280078887939453, "logps/chosen": -336.0633850097656, "logps/rejected": -262.0978088378906, "logits/chosen": -1.348737359046936, "logits/rejected": -1.3422983884811401, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.556640625, "stability/repetition_rate_mean": 0.805468738079071}
{"current_steps": 870, "total_steps": 927, "loss": 0.4598, "accuracy": 0.7953125238418579, "lr": 5.942995784154692e-09, "epoch": 0.9391445149102685, "percentage": 93.85, "elapsed_time": "3:39:01", "remaining_time": "0:14:20", "rewards/chosen": 1.0478211641311646, "rewards/rejected": -0.0012408018810674548, "rewards/accuracies": 0.7953125238418579, "rewards/margins": 1.0490620136260986, "logps/chosen": -307.34771728515625, "logps/rejected": -260.82586669921875, "logits/chosen": -1.3338909149169922, "logits/rejected": -1.3578466176986694, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.4718749523162842, "stability/repetition_rate_mean": 0.864453136920929}
{"current_steps": 880, "total_steps": 927, "loss": 0.4837, "accuracy": 0.7750000357627869, "lr": 4.075459886973082e-09, "epoch": 0.949939279449467, "percentage": 94.93, "elapsed_time": "3:41:22", "remaining_time": "0:11:49", "rewards/chosen": 0.9852436184883118, "rewards/rejected": 0.012728266417980194, "rewards/accuracies": 0.7750000357627869, "rewards/margins": 0.9725152850151062, "logps/chosen": -288.3506774902344, "logps/rejected": -259.43109130859375, "logits/chosen": -1.322386622428894, "logits/rejected": -1.3134368658065796, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.583984375, "stability/repetition_rate_mean": 0.8818359375}
{"current_steps": 890, "total_steps": 927, "loss": 0.4647, "accuracy": 0.8109375238418579, "lr": 2.556837798739886e-09, "epoch": 0.9607340439886655, "percentage": 96.01, "elapsed_time": "3:43:40", "remaining_time": "0:09:17", "rewards/chosen": 0.9938045740127563, "rewards/rejected": -0.0041345953941345215, "rewards/accuracies": 0.8109375238418579, "rewards/margins": 0.9979391098022461, "logps/chosen": -314.2873229980469, "logps/rejected": -257.3379821777344, "logits/chosen": -1.3387622833251953, "logits/rejected": -1.34397554397583, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.6566405296325684, "stability/repetition_rate_mean": 0.8443359136581421}
{"current_steps": 900, "total_steps": 927, "loss": 0.455, "accuracy": 0.785937488079071, "lr": 1.3892841162143899e-09, "epoch": 0.9715288085278639, "percentage": 97.09, "elapsed_time": "3:46:00", "remaining_time": "0:06:46", "rewards/chosen": 0.9897605776786804, "rewards/rejected": -0.039306994527578354, "rewards/accuracies": 0.785937488079071, "rewards/margins": 1.0290673971176147, "logps/chosen": -304.279541015625, "logps/rejected": -257.1634216308594, "logits/chosen": -1.3134535551071167, "logits/rejected": -1.3294223546981812, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.3954589366912842, "stability/repetition_rate_mean": 0.7369140386581421}
{"current_steps": 900, "total_steps": 927, "eval_loss": 0.46732690930366516, "epoch": 0.9715288085278639, "percentage": 97.09, "elapsed_time": "3:50:30", "remaining_time": "0:06:54"}
{"current_steps": 910, "total_steps": 927, "loss": 0.4964, "accuracy": 0.7562500238418579, "lr": 5.744553459100243e-10, "epoch": 0.9823235730670625, "percentage": 98.17, "elapsed_time": "3:52:57", "remaining_time": "0:04:21", "rewards/chosen": 0.9547117352485657, "rewards/rejected": 0.03406943380832672, "rewards/accuracies": 0.7562500238418579, "rewards/margins": 0.9206423163414001, "logps/chosen": -324.1401062011719, "logps/rejected": -261.5168762207031, "logits/chosen": -1.3120925426483154, "logits/rejected": -1.3330440521240234, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 1.468652367591858, "stability/repetition_rate_mean": 0.875195324420929}
{"current_steps": 920, "total_steps": 927, "loss": 0.4515, "accuracy": 0.784375011920929, "lr": 1.1350755386951849e-10, "epoch": 0.993118337606261, "percentage": 99.24, "elapsed_time": "3:55:26", "remaining_time": "0:01:47", "rewards/chosen": 1.044679045677185, "rewards/rejected": 0.007530394475907087, "rewards/accuracies": 0.784375011920929, "rewards/margins": 1.0371487140655518, "logps/chosen": -329.8288879394531, "logps/rejected": -261.056640625, "logits/chosen": -1.3584011793136597, "logits/rejected": -1.3463481664657593, "stability/response_length_mean": 512.0, "stability/response_length_std": 0.0, "stability/response_length_var": 0.0, "stability/token_entropy_mean": 2.8033204078674316, "stability/repetition_rate_mean": 0.861328125}
{"current_steps": 927, "total_steps": 927, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "3:59:14", "remaining_time": "0:00:00"}

2036
trainer_state.json Normal file
View File

File diff suppressed because it is too large Load Diff

3
training_args.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b0393bae99a1b9894faa98e056a587dea8db45430d4deb4e7d04eba3f18e2913
size 8145

BIN
training_eval_loss.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 34 KiB

BIN
training_loss.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 43 KiB

BIN
training_rewards_accuracies.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 47 KiB

BIN
training_rewards_chosen.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 42 KiB

BIN
training_rewards_margins.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 44 KiB

BIN
training_rewards_rejected.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 56 KiB