suehyunpark
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Commit
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Model save
Browse files- README.md +57 -0
- all_results.json +9 -0
- generation_config.json +12 -0
- train_results.json +9 -0
- trainer_state.json +71 -0
README.md
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---
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base_model: barc0/Llama-3.1-ARC-Potpourri-Induction-8B
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library_name: transformers
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model_name: potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task
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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 potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task
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This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Induction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Induction-8B).
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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="suehyunpark/potpourri-8b-inst-fft-induction-bc-trajectory-max1-per-task", 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/suehyun/arc-improve/runs/38sygte8)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.12.1
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- Transformers: 4.46.2
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- Pytorch: 2.5.1
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- Datasets: 3.1.0
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- Tokenizers: 0.20.3
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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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"epoch": 1.0,
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"total_flos": 1256277934080.0,
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"train_loss": 0.8235548933347067,
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"train_runtime": 340.6476,
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"train_samples": 169,
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"train_samples_per_second": 0.208,
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"train_steps_per_second": 0.009
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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": "4.46.2"
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}
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train_results.json
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{
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"epoch": 1.0,
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"total_flos": 1256277934080.0,
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"train_loss": 0.8235548933347067,
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"train_runtime": 340.6476,
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"train_samples": 169,
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"train_samples_per_second": 0.208,
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"train_steps_per_second": 0.009
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}
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trainer_state.json
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 1.0,
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"eval_steps": 500,
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"global_step": 3,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [
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{
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"epoch": 0.3333333333333333,
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"grad_norm": 104.34296088799998,
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"learning_rate": 1e-05,
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"loss": 1.0512,
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"step": 1
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},
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{
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"learning_rate": 5e-06,
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"loss": 1.0267,
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"step": 2
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},
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{
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"epoch": 1.0,
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"grad_norm": 7.664500232897243,
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"learning_rate": 0.0,
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"loss": 0.3927,
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"step": 3
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},
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{
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"epoch": 1.0,
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"eval_loss": 0.4052319824695587,
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"eval_runtime": 1.4338,
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"eval_samples_per_second": 2.092,
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"eval_steps_per_second": 0.697,
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"step": 3
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},
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{
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"train_runtime": 340.6476,
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"train_samples_per_second": 0.208,
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"train_steps_per_second": 0.009
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}
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],
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"logging_steps": 1,
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"max_steps": 3,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 1,
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"save_steps": 500,
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"stateful_callbacks": {
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"TrainerControl": {
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"args": {
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"should_epoch_stop": false,
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"should_evaluate": false,
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"should_log": false,
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"should_save": true,
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"should_training_stop": true
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},
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"attributes": {}
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}
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},
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}
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