Instructions to use bitext/Mistral-7B-Retail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bitext/Mistral-7B-Retail with Transformers:

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

pipe = pipeline("text-generation", model="bitext/Mistral-7B-Retail")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bitext/Mistral-7B-Retail")
model = AutoModelForCausalLM.from_pretrained("bitext/Mistral-7B-Retail")
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 bitext/Mistral-7B-Retail with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bitext/Mistral-7B-Retail

SGLang

How to use bitext/Mistral-7B-Retail 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 "bitext/Mistral-7B-Retail" \
    --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": "bitext/Mistral-7B-Retail",
		"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 "bitext/Mistral-7B-Retail" \
        --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": "bitext/Mistral-7B-Retail",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bitext/Mistral-7B-Retail with Docker Model Runner:
```
docker model run hf.co/bitext/Mistral-7B-Retail
```

Mistral-7B-Retail

Commit History

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Commit History

Adding `safetensors` variant of this model 7a206cc verified

Update README.md a91ace9 verified

Update README.md 6f0603c verified

Update README.md 168f094 verified

Update README.md 004c089 verified

Update README.md 7bcceda verified

Create README.md 76221c9 verified

Upload folder using huggingface_hub 19620f2 verified

initial commit eeb5661 verified

Adding `safetensors` variant of this model

7a206cc
verified

Update README.md

a91ace9
verified

Update README.md

6f0603c
verified

Update README.md

168f094
verified

Update README.md

004c089
verified

Update README.md

7bcceda
verified

Create README.md

76221c9
verified

Upload folder using huggingface_hub

19620f2
verified

initial commit

eeb5661
verified