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Model: UCLA-AGI/zephyr-7b-sft-full-SPIN-iter0 Source: Original Platform
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README.md
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README.md
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---
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license: mit
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datasets:
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- UCLA-AGI/SPIN_iter0
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language:
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- en
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pipeline_tag: text-generation
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---
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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models (https://arxiv.org/abs/2401.01335)
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# zephyr-7b-sft-full-spin-iter0
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This model is a self-play fine-tuned model at iteration 0 from [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) using synthetic data based on on the [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
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## Model Details
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### Model Description
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- Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
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- Language(s) (NLP): Primarily English
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- License: MIT
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- Finetuned from model: alignment-handbook/zephyr-7b-sft-full (based on mistralai/Mistral-7B-v0.1)
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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- train_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 64
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- optimizer: RMSProp
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2.0
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## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test0)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 62.37 |
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| ARC (25-shot) | 63.65 |
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| HellaSwag (10-shot) | 84.44 |
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| MMLU (5-shot) | 61.01 |
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| TruthfulQA (0-shot) | 50.48 |
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| Winogrande (5-shot) | 77.98 |
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| GSM8K (5-shot) | 36.69 |
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## Citation
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```
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@misc{chen2024selfplay,
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title={Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models},
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author={Zixiang Chen and Yihe Deng and Huizhuo Yuan and Kaixuan Ji and Quanquan Gu},
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year={2024},
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eprint={2401.01335},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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"architectures": [
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"MistralForCausalLM"
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],
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"hidden_act": "silu",
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||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
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|
||||
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|
||||
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||||
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||||
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|
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|
||||
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|
||||
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||||
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|
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|
||||
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|
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|
||||
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||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
91122
tokenizer.json
Normal file
91122
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
41
tokenizer_config.json
Normal file
41
tokenizer_config.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 2048,
|
||||
"pad_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
2443
trainer_state.json
Normal file
2443
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f09f6ebae60f5cf7aa9bc7b8e6d25455a48190b29ccdc77065805f0e5acf97ca
|
||||
size 6008
|
||||
Reference in New Issue
Block a user