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

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@@ -44,7 +44,7 @@ The "Mistral-7B-Retail-v2" uses the `MistralForCausalLM` structure with a `Llama
 This model was trained with a dataset specifically designed for retail-related question and answer interactions. The dataset encompasses a comprehensive range of retail intents, ensuring the model is trained to handle diverse customer inquiries and scenarios. It includes 46 distinct intents such as `add_product`, `availability_in_store`, `cancel_order`, `pay`, `refund_policy`, `track_order`, `use_app`, and many more, reflecting common retail transactions and customer service interactions. Each intent contains 1000 examples, which helps in creating responses across various retail situations.
-This extensive training dataset ensures that the model can understand and respond to a wide array of retail-related queries, providing support in customer service applications. The dataset follows a structured approach, similar to other datasets published on Hugging Face, but is specifically tailored to cater to the retail sector.
 ## Training Procedure

 This model was trained with a dataset specifically designed for retail-related question and answer interactions. The dataset encompasses a comprehensive range of retail intents, ensuring the model is trained to handle diverse customer inquiries and scenarios. It includes 46 distinct intents such as `add_product`, `availability_in_store`, `cancel_order`, `pay`, `refund_policy`, `track_order`, `use_app`, and many more, reflecting common retail transactions and customer service interactions. Each intent contains 1000 examples, which helps in creating responses across various retail situations.
+This extensive training dataset ensures that the model can understand and respond to a wide array of retail-related queries, providing support in customer service applications. The dataset follows a structured approach, similar to other datasets published on Hugging Face, but is specifically tailored to cater to the customer support sector: [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset)
 ## Training Procedure