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

Libraries

How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="DanielClough/Candle_Mistral-7B-Instruct-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DanielClough/Candle_Mistral-7B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("DanielClough/Candle_Mistral-7B-Instruct-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DanielClough/Candle_Mistral-7B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DanielClough/Candle_Mistral-7B-Instruct-v0.1

SGLang

How to use DanielClough/Candle_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 "DanielClough/Candle_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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DanielClough/Candle_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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/DanielClough/Candle_Mistral-7B-Instruct-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Here we have .gguf and .safetensors of mistralai/Mistral-Instruct-v0.1 for use with Huggingface/Candle.

You can try the models with Candle Chat, or make similar models with Candle Tensor Tools.

Refer to the main model card.

Downloads last month: 366

GGUF

Model size

7B params

Architecture

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

View +7 variants

Model tree for DanielClough/Candle_Mistral-7B-Instruct-v0.1

Quantizations

2 models