Model save
Browse files- README.md +58 -0
- all_results.json +8 -0
- generation_config.json +14 -0
- train_results.json +8 -0
- trainer_state.json +59 -0
README.md
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---
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base_model: Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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model_name: Qwen2.5-3B-Instruct-Distill-om220k-2k-origin-batch32-epoch1-8192
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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 Qwen2.5-3B-Instruct-Distill-om220k-2k-origin-batch32-epoch1-8192
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This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-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="Lansechen/Qwen2.5-3B-Instruct-Distill-om220k-2k-origin-batch32-epoch1-8192", 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/chenran1995-the-chinese-university-of-hong-kong/huggingface/runs/v5x4kmau)
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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+cu121
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- Datasets: 3.3.1
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- Tokenizers: 0.21.0
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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": 16534550347776.0,
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"train_loss": 0.7938935810869391,
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"train_runtime": 319.9151,
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"train_samples": 2080,
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"train_samples_per_second": 4.701,
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"train_steps_per_second": 0.034
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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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": 16534550347776.0,
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"train_loss": 0.7938935810869391,
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"train_runtime": 319.9151,
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"train_samples": 2080,
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"train_samples_per_second": 4.701,
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"train_steps_per_second": 0.034
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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": 0.9361702127659575,
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"eval_steps": 500,
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"global_step": 11,
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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": 0.425531914893617,
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"grad_norm": 1.173352837562561,
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"learning_rate": 3.4452882373436316e-05,
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"loss": 0.8972,
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"mean_token_accuracy": 0.7616663560271263,
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"step": 5
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},
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{
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"epoch": 0.851063829787234,
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"grad_norm": 0.5799760818481445,
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"learning_rate": 6.1012283833590465e-06,
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"loss": 0.7155,
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"mean_token_accuracy": 0.791662546992302,
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"step": 10
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},
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{
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"epoch": 0.9361702127659575,
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"mean_token_accuracy": 0.8011733368039131,
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"step": 11,
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"total_flos": 16534550347776.0,
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"train_loss": 0.7938935810869391,
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"train_runtime": 319.9151,
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"train_samples_per_second": 4.701,
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"train_steps_per_second": 0.034
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}
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],
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"logging_steps": 5,
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"max_steps": 11,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 1,
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"save_steps": 100,
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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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"total_flos": 16534550347776.0,
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"train_batch_size": 4,
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"trial_name": null,
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"trial_params": null
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}
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