Instructions to use hflog/mzbac-Kunpeng-4x7B-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hflog/mzbac-Kunpeng-4x7B-mistral with Transformers:

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

pipe = pipeline("text-generation", model="hflog/mzbac-Kunpeng-4x7B-mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("hflog/mzbac-Kunpeng-4x7B-mistral")
model = AutoModelForMultimodalLM.from_pretrained("hflog/mzbac-Kunpeng-4x7B-mistral")
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 hflog/mzbac-Kunpeng-4x7B-mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hflog/mzbac-Kunpeng-4x7B-mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hflog/mzbac-Kunpeng-4x7B-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hflog/mzbac-Kunpeng-4x7B-mistral

SGLang

How to use hflog/mzbac-Kunpeng-4x7B-mistral 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 "hflog/mzbac-Kunpeng-4x7B-mistral" \
    --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": "hflog/mzbac-Kunpeng-4x7B-mistral",
		"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 "hflog/mzbac-Kunpeng-4x7B-mistral" \
        --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": "hflog/mzbac-Kunpeng-4x7B-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hflog/mzbac-Kunpeng-4x7B-mistral with Docker Model Runner:
```
docker model run hf.co/hflog/mzbac-Kunpeng-4x7B-mistral
```

Kunpeng-4x7B-mistral

Architecture: Mixture of Experts (MoE)

A Moe Model of "Mistral-7B-Instruct-v0.2", "Mistral-7B-v0.1", "Starling-LM-7B-alpha", and "Mistral-7B-Instruct-v0.1" then fine-tuned with "WizardLM_evol_instruct_70k" for q_proj, v_proj, and gate.

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