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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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base_model: mistralai/Mistral-7B-v0.1 |
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model-index: |
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- name: mistral-7b-sft-beta |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Model Card for Mistral 7B SFT β |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset. It is the SFT model that was used to train Zephyr-7B-β with Direct Preference Optimization. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9399 |
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## Model description |
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- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. |
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- **Language(s) (NLP):** Primarily English |
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- **License:** MIT |
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- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/huggingface/alignment-handbook |
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## Intended uses & limitations |
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The model was fine-tuned with [🤗 TRL's](https://github.com/huggingface/trl) `SFTTrainer` on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. |
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
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```python |
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# Install transformers from source - only needed for versions <= v4.34 |
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# pip install git+https://github.com/huggingface/transformers.git |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="HuggingFaceH4/mistral-7b-sft-beta", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a friendly chatbot who always responds in the style of a pirate", |
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}, |
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{"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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# <|system|> |
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# You are a friendly chatbot who always responds in the style of a pirate.</s> |
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# <|user|> |
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# How many helicopters can a human eat in one sitting?</s> |
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# <|assistant|> |
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# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 16 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.9367 | 0.67 | 272 | 0.9397 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.14.0 |
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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_HuggingFaceH4__mistral-7b-sft-beta) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |59.78| |
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|AI2 Reasoning Challenge (25-Shot)|57.42| |
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|HellaSwag (10-Shot) |82.23| |
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|MMLU (5-Shot) |61.42| |
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|TruthfulQA (0-shot) |43.58| |
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|Winogrande (5-shot) |77.58| |
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|GSM8k (5-shot) |36.47| |
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