This model was published in HF papers on 2023-10-10 and contributed to Hugging Face Transformers on 2023-09-27.
Mistral is a 7B parameter language model, available as a pretrained and instruction-tuned variant, focused on balancing 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.
You can find all the original Mistral checkpoints under the Mistral AI_ organization.
Tip
Click on the Mistral models in the right sidebar for more examples of how to apply Mistral to different language tasks.
Set use_kernels=True in [~PreTrainedModel.from_pretrained] to replace supported layers with optimized kernels from the Hub. Refer to Loading kernels to learn more.
The example below demonstrates how to chat with [Pipeline] or the [AutoModel], and from the command line.
from transformers import pipeline
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"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!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3", device=0)
chatbot(messages)from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", attn_implementation="sdpa", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"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!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
tokenizer.batch_decode(generated_ids)[0]
"Mayonnaise can be made as follows: (...)"echo -e "My favorite condiment is" | transformers chat mistralai/Mistral-7B-v0.3 --dtype auto --device 0 --attn_implementation flash_attention_2Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.
The example below uses bitsandbytes to only quantize the weights to 4-bits.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# specify how to quantize the model
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", quantization_config=quantization_config, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
prompt = "My favourite condiment is"
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"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!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
tokenizer.batch_decode(generated_ids)[0]
"The expected output"Use the AttentionMaskVisualizer to better understand what tokens the model can and cannot attend to.
from transformers.utils.attention_visualizer import AttentionMaskVisualizer
visualizer = AttentionMaskVisualizer("mistralai/Mistral-7B-Instruct-v0.3")
visualizer("Do you have mayonnaise recipes?")
[[autodoc]] MistralConfig
[[autodoc]] MistralCommonBackend
[[autodoc]] MistralModel - forward
[[autodoc]] MistralForCausalLM - forward
[[autodoc]] MistralForSequenceClassification - forward
[[autodoc]] MistralForTokenClassification - forward
[[autodoc]] MistralForQuestionAnswering - forward