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
```

Bitext commited on May 14, 2024

Commit

168f094

verified ·

1 Parent(s): 004c089

Update README.md

Browse files

Fix scope for retail

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -65,12 +65,12 @@ This extensive training dataset ensures that the model can understand and respon
 ## Limitations and Bias
-- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of financial advice.
-- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. This model has been trained with a dataset that answers general wealth management questions, so potential biases may exist for specific use cases.
 ## Ethical Considerations
-This model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this financial field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant financial regulations.
 ## Acknowledgments

 ## Limitations and Bias
+- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of retail advice.
+- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. This model has been trained with a dataset that answers general retail questions, so potential biases may exist for specific use cases.
 ## Ethical Considerations
+This model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this retail field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant retail regulations.
 ## Acknowledgments