Instructions to use Infinigence/Megrez2-3x7B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Infinigence/Megrez2-3x7B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="Infinigence/Megrez2-3x7B-A3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Infinigence/Megrez2-3x7B-A3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Infinigence/Megrez2-3x7B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Infinigence/Megrez2-3x7B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Infinigence/Megrez2-3x7B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Infinigence/Megrez2-3x7B-A3B

SGLang

How to use Infinigence/Megrez2-3x7B-A3B 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 "Infinigence/Megrez2-3x7B-A3B" \
    --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": "Infinigence/Megrez2-3x7B-A3B",
		"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 "Infinigence/Megrez2-3x7B-A3B" \
        --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": "Infinigence/Megrez2-3x7B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Infinigence/Megrez2-3x7B-A3B with Docker Model Runner:
```
docker model run hf.co/Infinigence/Megrez2-3x7B-A3B
```

liyadong commited on Sep 15, 2025

Commit

af1cd40

1 Parent(s): b20e44e

update llama.cpp note

Browse files

Files changed (2) hide show

README.md +1 -1
README_ZH.md +2 -1

README.md CHANGED Viewed

@@ -209,7 +209,7 @@ from modelscope import AutoModelForCausalLM, AutoTokenizer
 ### llama.cpp
-Coming soon...
 ## How to Deploy

 ### llama.cpp
+llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. Now supported, please refer to the [support-megrez branch](https://github.com/infinigence/llama.cpp/tree/support-megrez) for details.
 ## How to Deploy

README_ZH.md CHANGED Viewed

@@ -196,7 +196,8 @@ from modelscope import AutoModelForCausalLM, AutoTokenizer
 ```
 ### llama.cpp
-即将到来...
 ## 如何部署

 ```
 ### llama.cpp
+llama.cpp 支持在各种硬件上以最小的设置和最先进的性能来启用LLM推断。现已支持，具体请查看 [support-megrez 分支](https://github.com/infinigence/llama.cpp/tree/support-megrez)。
 ## 如何部署