Instructions to use mistralai/Mistral-7B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mistralai/Mistral-7B-Instruct-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mistralai/Mistral-7B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "mistralai/Mistral-7B-Instruct-v0.1" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.1

SGLang

How to use mistralai/Mistral-7B-Instruct-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mistralai/Mistral-7B-Instruct-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mistralai/Mistral-7B-Instruct-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mistralai/Mistral-7B-Instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mistralai/Mistral-7B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/mistralai/Mistral-7B-Instruct-v0.1
```

Mistral-7B-Instruct-v0.1 / README.md

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	---
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- finetuned
	inference: true
	widget:
	- messages:
	- role: user
	content: What is your favorite condiment?
	---

	# Model Card for Mistral-7B-Instruct-v0.1

	The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.

	For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).

	## Instruction format

	In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

	E.g.
	```
	text = "<s>[INST] What is your favourite condiment? [/INST]"
	"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!</s> "
	"[INST] Do you have mayonnaise recipes? [/INST]"
	```

	This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	device = "cuda" # the device to load the model onto

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

	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?"}
	]

	encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

	model_inputs = encodeds.to(device)
	model.to(device)

	generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
	decoded = tokenizer.batch_decode(generated_ids)
	print(decoded[0])
	```

	## Model Architecture
	This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
	- Grouped-Query Attention
	- Sliding-Window Attention
	- Byte-fallback BPE tokenizer

	## Troubleshooting
	- If you see the following error:
	```
	Traceback (most recent call last):
	File "", line 1, in
	File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
	config, kwargs = AutoConfig.from_pretrained(
	File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
	config_class = CONFIG_MAPPING[config_dict["model_type"]]
	File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
	raise KeyError(key)
	KeyError: 'mistral'
	```

	Installing transformers from source should solve the issue
	pip install git+https://github.com/huggingface/transformers

	This should not be required after transformers-v4.33.4.

	## Limitations

	The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
	It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
	make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

	## The Mistral AI 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.