transformers/docs/source/en/model_doc/mistral.md

Mistral

Overview

Mistral-7B-v0.1 is Mistral AI's first Large Language Model (LLM).

Model Details

Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices:

• Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
• GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
• Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.

We also provide an instruction fine-tuned model: Mistral-7B-Instruct-v0.1 which can be used for chat-based inference.

For more details please read our release blog post

License

Both Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 are released under the Apache 2.0 license.

Usage tips

Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be found on the Huggingface Hub

These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:

>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

>>> prompt = "My favourite condiment is"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"

Raw weights for Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be downloaded from:

Model Name	Checkpoint
Mistral-7B-v0.1	Raw Checkpoint
Mistral-7B-Instruct-v0.1	Raw Checkpoint

To use these raw checkpoints with HuggingFace you can use the convert_mistral_weights_to_hf.py script to convert them to the HuggingFace format:

python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
    --input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path

You can then load the converted model from the output/path:

from transformers import MistralForCausalLM, LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained("/output/path")
model = MistralForCausalLM.from_pretrained("/output/path")

Combining Mistral and Flash Attention 2

First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.

pip install -U flash-attn --no-build-isolation

Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. torch.float16)

To load and run a model using Flash Attention 2, refer to the snippet below:

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

>>> prompt = "My favourite condiment is"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"

Expected speedups

Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using mistralai/Mistral-7B-v0.1 checkpoint and the Flash Attention 2 version of the model.

Sliding window Attention

The current implementation supports the sliding window attention mechanism and memory efficient cache management. To enable sliding window attention, just make sure to have a flash-attn version that is compatible with sliding window attention (>=2.3.0).

The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (self.config.sliding_window), support batched generation only for padding_side="left" and use the absolute position of the current token to compute the positional embedding.

The Mistral Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

MistralConfig

autodoc MistralConfig

MistralModel

autodoc MistralModel - forward

MistralForCausalLM

autodoc MistralForCausalLM - forward

MistralForSequenceClassification

autodoc MistralForSequenceClassification - forward

FlaxMistralModel

autodoc FlaxMistralModel - call

FlaxMistralForCausalLM

autodoc FlaxMistralForCausalLM - call