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* model card for Mistral * Update docs/source/en/model_doc/mistral.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/mistral.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/mistral.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/mistral.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/mistral.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * apply suggestions * fix typo * updated with comments * updated with comments * updated with comments * remove hfoption block --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
190 lines
10 KiB
Markdown
190 lines
10 KiB
Markdown
<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# Mistral
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[Mistral](https://huggingface.co/papers/2310.06825) is a 7B parameter language model, available as a pretrained and instruction-tuned variant, focused on balancing
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the scaling costs of large models with performance and efficient inference. This model uses sliding window attention (SWA) trained with a 8K context length and a fixed cache size to handle longer sequences more effectively. Grouped-query attention (GQA) speeds up inference and reduces memory requirements. Mistral also features a byte-fallback BPE tokenizer to improve token handling and efficiency by ensuring characters are never mapped to out-of-vocabulary tokens.
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You can find all the original Mistral checkpoints under the [Mistral AI_](https://huggingface.co/mistralai) organization.
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> [!TIP]
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> Click on the Mistral models in the right sidebar for more examples of how to apply Mistral to different language tasks.
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The example below demonstrates how to chat with [`Pipeline`] or the [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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>>> import torch
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>>> from transformers import pipeline
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>>> messages = [
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... {"role": "user", "content": "What is your favourite condiment?"},
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... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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... {"role": "user", "content": "Do you have mayonnaise recipes?"}
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... ]
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>>> chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, device=0)
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>>> chatbot(messages)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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>>> import torch
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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>>> messages = [
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... {"role": "user", "content": "What is your favourite condiment?"},
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... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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... {"role": "user", "content": "Do you have mayonnaise recipes?"}
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... ]
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>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
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>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids)[0]
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"Mayonnaise can be made as follows: (...)"
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```
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</hfoption>
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<hfoption id="transformers-cli">
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```python
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echo -e "My favorite condiment is" | transformers-cli chat --model_name_or_path mistralai/Mistral-7B-v0.3 --torch_dtype auto --device 0 --attn_implementation flash_attention_2
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.
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```python
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>>> import torch
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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>>> # specify how to quantize the model
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>>> quantization_config = BitsAndBytesConfig(
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... load_in_4bit=True,
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... bnb_4bit_quant_type="nf4",
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... bnb_4bit_compute_dtype="torch.float16",
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... )
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>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", quantization_config=True, torch_dtype=torch.bfloat16, device_map="auto")
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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>>> prompt = "My favourite condiment is"
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>>> messages = [
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... {"role": "user", "content": "What is your favourite condiment?"},
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... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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... {"role": "user", "content": "Do you have mayonnaise recipes?"}
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... ]
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>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
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>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids)[0]
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"The expected output"
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```
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Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
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```py
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>>> from transformers.utils.attention_visualizer import AttentionMaskVisualizer
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>>> visualizer = AttentionMaskVisualizer("mistralai/Mistral-7B-Instruct-v0.3")
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>>> visualizer("Do you have mayonnaise recipes?")
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mistral-attn-mask.png"/>
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</div>
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## MistralConfig
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[[autodoc]] MistralConfig
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## MistralModel
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[[autodoc]] MistralModel
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- forward
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## MistralForCausalLM
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[[autodoc]] MistralForCausalLM
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- forward
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## MistralForSequenceClassification
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[[autodoc]] MistralForSequenceClassification
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- forward
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## MistralForTokenClassification
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[[autodoc]] MistralForTokenClassification
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- forward
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## MistralForQuestionAnswering
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[[autodoc]] MistralForQuestionAnswering
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- forward
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## FlaxMistralModel
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[[autodoc]] FlaxMistralModel
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- __call__
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## FlaxMistralForCausalLM
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[[autodoc]] FlaxMistralForCausalLM
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- __call__
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## TFMistralModel
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[[autodoc]] TFMistralModel
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- call
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## TFMistralForCausalLM
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[[autodoc]] TFMistralForCausalLM
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- call
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## TFMistralForSequenceClassification
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[[autodoc]] TFMistralForSequenceClassification
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- call
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