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Model: ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated
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2026-04-13 08:58:58 +08:00
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---
base_model: PleIAs/Baguettotron
library_name: transformers
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for None
This model is a fine-tuned version of [PleIAs/Baguettotron](https://huggingface.co/PleIAs/Baguettotron).
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 SFT.
### 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 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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"train_samples_per_second": 71.316,
"train_steps_per_second": 0.557
}

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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": true,
"vocab_size": 65600
}

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model_name_or_path: PleIAs/Baguettotron
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/tulu-3-sft-mixture-decontaminated
config: default
split: train
columns:
- messages
dataset_num_proc: 32
eval_strategy: 'no'
remove_unused_columns: true
dataset_kwargs:
add_special_tokens: false
append_concat_token: false
output_dir: /e/project1/reformo/ali/alignment-handbook/results/mv_exp/sft/PleIAs-Baguettotron_tulu-3-sft-mixture-decontaminated_GH200
overwrite_output_dir: false
save_strategy: steps
save_steps: 200
save_total_limit: 2
push_to_hub: false
log_level: info
report_to:
- wandb
logging_steps: 10
logging_strategy: steps
seed: 42
bf16: true
gradient_checkpointing: true
learning_rate: 5.0e-06
lr_scheduler_type: linear
max_length: 4096
warmup_ratio: 0.03
num_train_epochs: 2
per_device_train_batch_size: 16
gradient_accumulation_steps: 1
hub_model_id: ali-elganzory/Baguettotron-SFT-Tulu3-decontaminated
hub_strategy: every_save

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{
"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 3,
"transformers_version": "4.57.6"
}

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{
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}

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