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

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
```

System Prompt

#41

by sakshat98 - opened Oct 5, 2023

Discussion

sakshat98

Oct 5, 2023

Hi, I love Mistral. I just had a use case where I wanted to have a system prompt during conversation. I saw that Mistral does not accept {""role":"system"}. Is there any way to have this?

techandy42

Oct 8, 2023

Try the following

sys_prompt = "You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old."
prompt = "What kind of task would test someone's ability to perform physical reasoning?"

prefix = "<|im_start|>"
suffix = "<|im_end|>\n"
sys_format = prefix + "system\n" + sys_prompt + suffix
user_format = prefix + "user\n" + prompt + suffix
assistant_format = prefix + "assistant\n"
input_text = sys_format + user_format + assistant_format

messages = [
{"role": "user", "content": input_text},
]

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])

pvbhanuteja

Oct 12, 2023

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:])

chujiezheng

Dec 21, 2023

I collected official chat templates in this repo. You may use it with the apply_chat_template method

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