104 lines
3.5 KiB
Markdown
104 lines
3.5 KiB
Markdown
---
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license: mit
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base_model: mistralai/Mistral-7B-v0.1
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tags:
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- generated_from_trainer
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model-index:
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- name: mistral-7b-sft-alpha
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results: []
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datasets:
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- stingning/ultrachat
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language:
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- en
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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 α
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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 UltraChat dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9316
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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-alpha", 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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| 0.9276 | 0.66 | 296 | 0.9315 |
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### Framework versions
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- Transformers 4.34.0
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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 |