Model save
Browse files- README.md +58 -0
- all_results.json +8 -0
- generation_config.json +12 -0
- train_results.json +8 -0
- trainer_state.json +455 -0
README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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model_name: Llama3.1-8B-WebArena-Lite-SFT-CoT-QwQ-32B-epoch-1
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for Llama3.1-8B-WebArena-Lite-SFT-CoT-QwQ-32B-epoch-1
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="weizhepei/Llama3.1-8B-WebArena-Lite-SFT-CoT-QwQ-32B-epoch-1", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/uva-llm/huggingface/runs/pqr5httj)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.49.0
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- Pytorch: 2.5.1
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- Datasets: 3.4.1
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- Tokenizers: 0.21.1
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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 Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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all_results.json
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{
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"total_flos": 137372044492800.0,
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"train_loss": 0.8683076865366988,
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"train_runtime": 5339.1126,
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"train_samples": 9460,
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"train_samples_per_second": 1.772,
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"train_steps_per_second": 0.055
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.49.0"
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}
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train_results.json
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{
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"total_flos": 137372044492800.0,
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"train_loss": 0.8683076865366988,
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"train_runtime": 5339.1126,
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"train_samples": 9460,
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"train_samples_per_second": 1.772,
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"train_steps_per_second": 0.055
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}
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trainer_state.json
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{
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| 2 |
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"best_metric": null,
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| 3 |
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"best_model_checkpoint": null,
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| 4 |
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"epoch": 1.0,
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| 5 |
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"eval_steps": 500,
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| 6 |
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"global_step": 296,
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| 7 |
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"is_hyper_param_search": false,
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| 8 |
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"is_local_process_zero": true,
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| 9 |
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"is_world_process_zero": true,
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| 10 |
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"log_history": [
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| 11 |
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{
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| 12 |
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"epoch": 0.016891891891891893,
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| 13 |
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"grad_norm": 7.82988987444574,
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"learning_rate": 1.6666666666666667e-05,
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"loss": 1.2398,
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"step": 5
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},
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| 18 |
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{
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| 19 |
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"epoch": 0.033783783783783786,
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| 20 |
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"grad_norm": 3.3573634596072273,
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"learning_rate": 3.3333333333333335e-05,
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"loss": 1.0752,
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"step": 10
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},
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{
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"epoch": 0.05067567567567568,
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"grad_norm": 3.3415007647224853,
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"learning_rate": 5e-05,
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"loss": 1.0326,
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"step": 15
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| 31 |
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},
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| 32 |
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{
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| 33 |
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"epoch": 0.06756756756756757,
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"grad_norm": 2.908273898627516,
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"learning_rate": 4.9964854757667645e-05,
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"loss": 1.0739,
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"step": 20
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| 38 |
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},
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| 39 |
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{
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| 40 |
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"epoch": 0.08445945945945946,
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| 41 |
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"grad_norm": 2.6965407638337644,
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"learning_rate": 4.985952882516468e-05,
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"loss": 1.0738,
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"step": 25
|
| 45 |
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},
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| 46 |
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{
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| 47 |
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"epoch": 0.10135135135135136,
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"grad_norm": 2.5010143912708998,
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"learning_rate": 4.9684351242973035e-05,
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| 50 |
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"loss": 1.0438,
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"step": 30
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| 52 |
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},
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| 53 |
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{
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| 54 |
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