Instructions to use HuggingFaceH4/mistral-7b-anthropic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/mistral-7b-anthropic with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/mistral-7b-anthropic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/mistral-7b-anthropic")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/mistral-7b-anthropic", device_map="auto")
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 Settings

vLLM

How to use HuggingFaceH4/mistral-7b-anthropic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/mistral-7b-anthropic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/mistral-7b-anthropic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/mistral-7b-anthropic

SGLang

How to use HuggingFaceH4/mistral-7b-anthropic 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 "HuggingFaceH4/mistral-7b-anthropic" \
    --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": "HuggingFaceH4/mistral-7b-anthropic",
		"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 "HuggingFaceH4/mistral-7b-anthropic" \
        --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": "HuggingFaceH4/mistral-7b-anthropic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceH4/mistral-7b-anthropic with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/mistral-7b-anthropic
```

lewtun HF Staff commited on Feb 1, 2024

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@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
 # Mistral 7B Constitutional AI
-This model is a DPO-aligned version of [HuggingFaceH4/mistral-7b-cai](https://huggingface.co/HuggingFaceH4/mistral-7b-cai) on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets.
 It achieves the following results on the evaluation set:
 - Loss: 0.6327

 # Mistral 7B Constitutional AI
+This model is a DPO-aligned version of Mistral 7B on the HuggingFaceH4/ultrafeedback_binarized_fixed and the HuggingFaceH4/cai-conversation-harmless datasets.
 It achieves the following results on the evaluation set:
 - Loss: 0.6327