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
```

Megrez2-3x7B-A3B / config.json

liyadong

add model file and update readme

b20e44e 11 months ago

Raw

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1.32 kB

	{
	"architectures": [
	"MegrezMoeForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_megrez_moe.MegrezMoeConfig",
	"AutoModel": "modeling_megrez_moe.MegrezMoeModel",
	"AutoModelForCausalLM": "modeling_megrez_moe.MegrezMoeForCausalLM"
	},
	"aux_loss_alpha": 0.001,
	"bos_token_id": null,
	"eos_token_id": 120005,
	"ep_size": 1,
	"experts_shared_frequency": 3,
	"first_k_dense_replace": 1,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 10944,
	"max_position_embeddings": 163840,
	"model_type": "megrez_moe",
	"moe_intermediate_size": 1408,
	"moe_layer_freq": 1,
	"n_group": 1,
	"n_routed_experts": 64,
	"n_shared_experts": 4,
	"norm_topk_prob": true,
	"num_attention_heads": 16,
	"num_experts_per_tok": 6,
	"num_hidden_layers": 11,
	"num_key_value_heads": 4,
	"pad_token_id": 120002,
	"pre_gate": true,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 5000000,
	"routed_scaling_factor": 1.0,
	"scoring_func": "sigmoid",
	"seq_aux": true,
	"tie_word_embeddings": false,
	"topk_group": 1,
	"topk_method": "noaux_tc",
	"torch_dtype": "bfloat16",
	"transformers_version": "4.52.4",
	"use_cache": true,
	"vocab_size": 122880
	}