Instructions to use Nondzu/Mistral-7B-Instruct-v0.2-code-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/Mistral-7B-Instruct-v0.2-code-ft with Transformers:

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

pipe = pipeline("text-generation", model="Nondzu/Mistral-7B-Instruct-v0.2-code-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nondzu/Mistral-7B-Instruct-v0.2-code-ft")
model = AutoModelForCausalLM.from_pretrained("Nondzu/Mistral-7B-Instruct-v0.2-code-ft")
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 Nondzu/Mistral-7B-Instruct-v0.2-code-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nondzu/Mistral-7B-Instruct-v0.2-code-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nondzu/Mistral-7B-Instruct-v0.2-code-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft

SGLang

How to use Nondzu/Mistral-7B-Instruct-v0.2-code-ft 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 "Nondzu/Mistral-7B-Instruct-v0.2-code-ft" \
    --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": "Nondzu/Mistral-7B-Instruct-v0.2-code-ft",
		"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 "Nondzu/Mistral-7B-Instruct-v0.2-code-ft" \
        --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": "Nondzu/Mistral-7B-Instruct-v0.2-code-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nondzu/Mistral-7B-Instruct-v0.2-code-ft with Docker Model Runner:
```
docker model run hf.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft
```

Awesome Model

by exerdj - opened Jan 26, 2024

Discussion

exerdj

Jan 26, 2024

I just wanted to say thank you for this model. I have been using it actively as assistant for my day to day coding work and it has been performing quite good.

And wanted to ask do you have plan to update the model in future?

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