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Model: ali-elganzory/Baguettotron-DPO-Tulu3-decontaminated
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---
base_model: ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated
library_name: transformers
model_name: Baguettotron-DPO-Tulu3-decontaminated
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for Baguettotron-DPO-Tulu3-decontaminated
This model is a fine-tuned version of [ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated](https://huggingface.co/ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated).
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="None", 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: 0.27.1
- Transformers: 4.57.6
- Pytorch: 2.6.0+cu126
- Datasets: 4.8.4
- 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}}
}
```

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"epoch": 1.0,
"total_flos": 0.0,
"train_loss": 4.036165593823357,
"train_runtime": 7155.9863,
"train_samples": 272585,
"train_samples_per_second": 38.092,
"train_steps_per_second": 0.298
}

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{%- for message in messages -%}
{%- if message["role"] == "system" -%}
{{- "<|system|>
" + message["content"] + "
" -}}
{%- elif message["role"] == "user" -%}
{{- "<|user|>
" + message["content"] + "
" -}}
{%- elif message["role"] == "assistant" -%}
{%- if not loop.last -%}
{{- "<|assistant|>
" + message["content"] + eos_token + "
" -}}
{%- else -%}
{{- "<|assistant|>
" + message["content"] + eos_token -}}
{%- endif -%}
{%- endif -%}
{%- if loop.last and add_generation_prompt -%}
{{- "<|assistant|>
" -}}
{%- endif -%}
{%- endfor -%}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"initializer_range": 0.02,
"intermediate_size": 1536,
"max_position_embeddings": 4096,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 9,
"num_hidden_layers": 80,
"num_key_value_heads": 3,
"pad_token_id": 3,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 10000,
"tie_word_embeddings": true,
"transformers_version": "4.57.6",
"use_cache": false,
"vocab_size": 65600
}

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model_name_or_path: ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated
dtype: bfloat16
attn_implementation: flash_attention_2
trust_remote_code: true
chat_template: "\n{%- for message in messages -%}\n\t{%- if message[\"role\"] == \"\
system\" -%}\n\t\t{{- \"<|system|>\n\" + message[\"content\"] + \"\n\" -}}\n\t{%-\
\ elif message[\"role\"] == \"user\" -%}\n\t\t{{- \"<|user|>\n\" + message[\"content\"\
] + \"\n\" -}}\n\t{%- elif message[\"role\"] == \"assistant\" -%}\n\t\t{%- if not\
\ loop.last -%}\n\t\t\t{{- \"<|assistant|>\n\" + message[\"content\"] + eos_token\
\ + \"\n\" -}}\n\t\t{%- else -%}\n\t\t\t{{- \"<|assistant|>\n\" + message[\"content\"\
] + eos_token -}}\n\t\t{%- endif -%}\n\t{%- endif -%}\n\t{%- if loop.last and add_generation_prompt\
\ -%}\n\t\t{{- \"<|assistant|>\n\" -}}\n\t{%- endif -%}\n{%- endfor -%}\n"
additional_special_tokens:
- <|system|>
- <|user|>
- <|assistant|>
dataset_mixture:
datasets:
- id: ali-elganzory/llama-3.1-tulu-3-8b-preference-mixture-decontaminated
config: default
split: train
columns:
- chosen
- rejected
dataset_num_proc: 32
remove_unused_columns: true
eval_strategy: 'no'
output_dir: /e/project1/reformo/ali/alignment-handbook/results/mv_exp/dpo/ali-elganzory-Baguettotron-SFT-Tulu3-decontaminated_tulu-3-8b-preference-mixture-decontaminated_GH200
overwrite_output_dir: false
save_strategy: steps
save_steps: 200
save_total_limit: 2
log_level: info
report_to:
- wandb
logging_steps: 10
logging_strategy: steps
seed: 42
bf16: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
learning_rate: 5.0e-07
lr_scheduler_type: linear
max_length: 2048
beta: 5
warmup_ratio: 0.1
per_device_train_batch_size: 32
gradient_accumulation_steps: 1
num_train_epochs: 1
hub_model_id: ali-elganzory/Baguettotron-DPO-Tulu3-decontaminated
hub_strategy: every_save

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{
"_from_model_config": true,
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"pad_token_id": 3,
"transformers_version": "4.57.6"
}

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