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
```

Possibilities injecting custom system into mistral?

#32

by TikaToka - opened Oct 2, 2023

Discussion

TikaToka

Oct 2, 2023

Hello mistralai team, Thank you for sharing amazing work!
I am hoping to use the model for my research, and I have some question.

For llama-2 like models often able to inject custom system prompt, but it looks like there are no information about it.
Is there a way to give custom system prompt to mistral?

sakshat98

Oct 5, 2023

I am looking for the same. Please update once you find something.

pvbhanuteja

Oct 12, 2023

@sakshat98 @TikaToka

Use this

~~[INST] System Prompt + Instruction [/INST] Model answer~~[INST] Follow-up instruction [/INST]

From their official site.

https://docs.mistral.ai/usage/guardrailing

If you want it for gradio. I wrote a formatting function. Remove/add accordingly.

def format_chat_prompt_mistral(message: str, chat_history, instructions: str) -> str:
    if len(chat_history) == 0:
        # If chat_history is empty, return instructions and message
        prompt = f"<s>[INST] {instructions} Hi [/INST] Hello! how can I help you</s>[INST] {message} [/INST]"
        print("sending this prompt\n==============\n",prompt,'\n---------\n')
        return prompt
    else:
        # Initialize chat history text with the first user message and instructions
        user_message, bot_message = chat_history[0]
        chat_history_text = f"<s>[INST] {instructions} {user_message} [/INST] {bot_message}</s>"

        # Use a list comprehension to build the rest of the chat history text
        chat_history_text += "".join(f"[INST] {user_message} [/INST] {bot_message}</s>" for user_message, bot_message in chat_history[1:])

Rocketknight1

Oct 16, 2023

Hi all, the tokenizer.apply_chat_template() method can handle this for you:

messages = [
    {"role": "system", "content": "System message here"},
    {"role": "user", "content:" "User message here"}
]
prompt = tokenizer.apply_chat_template(messages)

You can see the documentation on chat templates for more information.

TikaToka

Oct 17, 2023

•

edited Oct 17, 2023

@Rocketknight1 @pvbhanuteja Thank you for sharing the answer!
But I also want to ask how for huggingface inference api?

pvbhanuteja

Oct 18, 2023

Hi all, the tokenizer.apply_chat_template() method can handle this for you:
messages = [
    {"role": "system", "content": "System message here"},
    {"role": "user", "content:" "User message here"}
]
prompt = tokenizer.apply_chat_template(messages)
You can see the documentation on chat templates for more information.

This is incorrect. tokenizer.apply_chat_template will look chat_template key value from here at https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json

The format at chat_template doesn't incline with the actual mistral website. https://docs.mistral.ai/usage/guardrailing I will try to open a PR and make changes to the chat template accordingly.

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