Instructions to use vicky4s4s/mistral-7b-v2-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vicky4s4s/mistral-7b-v2-instruct with Transformers:

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

pipe = pipeline("text-generation", model="vicky4s4s/mistral-7b-v2-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vicky4s4s/mistral-7b-v2-instruct")
model = AutoModelForCausalLM.from_pretrained("vicky4s4s/mistral-7b-v2-instruct")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vicky4s4s/mistral-7b-v2-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vicky4s4s/mistral-7b-v2-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vicky4s4s/mistral-7b-v2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vicky4s4s/mistral-7b-v2-instruct

SGLang

How to use vicky4s4s/mistral-7b-v2-instruct 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 "vicky4s4s/mistral-7b-v2-instruct" \
    --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": "vicky4s4s/mistral-7b-v2-instruct",
		"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 "vicky4s4s/mistral-7b-v2-instruct" \
        --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": "vicky4s4s/mistral-7b-v2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vicky4s4s/mistral-7b-v2-instruct with Docker Model Runner:
```
docker model run hf.co/vicky4s4s/mistral-7b-v2-instruct
```

Use below code to download the mistral.


#pip install -U transformers accelerate torch

import torch
from transformers import pipeline, set_seed

model_path = "vicky4s4s/mistral-7b-v2-instruct"

pipe = pipeline("text-generation", model=model_path, torch_dtype=torch.bfloat16, device_map="cuda")
messages = [{"role": "user", "content": "what is meaning of life?"}]
outputs = pipe(messages, max_new_tokens=1000, do_sample=True, temperature=0.71, top_k=50, top_p=0.92,repetition_penalty=1)
print(outputs[0]["generated_text"][-1]["content"])

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.

Develop By

Vignesh, vickys9715@gmail.com

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16