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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bitext/Mistral-7B-Wealth_Management"
# 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-Wealth_Management",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Mistral-7B-Wealth_Management

Commit History

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Commit History

Adding `safetensors` variant of this model 08c0e3b verified

Update README.md 6f8477c verified

Update README.md 73b4395 verified

Update README.md b920f86 verified

Update README.md c27967f verified

Update README.md f9fafc9 verified

Create README.md 73f7853 verified

Upload folder using huggingface_hub 7ebc20b verified

initial commit 610063f verified

Adding `safetensors` variant of this model

08c0e3b
verified

Update README.md

6f8477c
verified

Update README.md

73b4395
verified

Update README.md

b920f86
verified

Update README.md

c27967f
verified

Update README.md

f9fafc9
verified

Create README.md

73f7853
verified

Upload folder using huggingface_hub

7ebc20b
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initial commit

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