Image-Text-to-Text
Transformers
minimax_m3_vl
multimodal
Mixture of Experts
agent
coding
video
conversational
custom_code
mxfp8
Instructions to use MiniMaxAI/MiniMax-M3-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiniMaxAI/MiniMax-M3-MXFP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MiniMaxAI/MiniMax-M3-MXFP8", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("MiniMaxAI/MiniMax-M3-MXFP8", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("MiniMaxAI/MiniMax-M3-MXFP8", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MiniMaxAI/MiniMax-M3-MXFP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MiniMaxAI/MiniMax-M3-MXFP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MiniMaxAI/MiniMax-M3-MXFP8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/MiniMaxAI/MiniMax-M3-MXFP8
- SGLang
How to use MiniMaxAI/MiniMax-M3-MXFP8 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 "MiniMaxAI/MiniMax-M3-MXFP8" \ --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": "MiniMaxAI/MiniMax-M3-MXFP8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "MiniMaxAI/MiniMax-M3-MXFP8" \ --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": "MiniMaxAI/MiniMax-M3-MXFP8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use MiniMaxAI/MiniMax-M3-MXFP8 with Docker Model Runner:
docker model run hf.co/MiniMaxAI/MiniMax-M3-MXFP8
| { | |
| "architectures": [ | |
| "MiniMaxM3SparseForConditionalGeneration" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_minimax_m3_vl.MiniMaxM3VLConfig" | |
| }, | |
| "model_type": "minimax_m3_vl", | |
| "text_config": { | |
| "dtype": "bfloat16", | |
| "hidden_size": 6144, | |
| "intermediate_size": 3072, | |
| "num_hidden_layers": 60, | |
| "num_attention_heads": 64, | |
| "num_key_value_heads": 4, | |
| "head_dim": 128, | |
| "vocab_size": 200064, | |
| "max_position_embeddings": 1048576, | |
| "rms_norm_eps": 1e-06, | |
| "use_gemma_norm": true, | |
| "attention_output_gate": false, | |
| "rope_theta": 5000000, | |
| "rotary_dim": 64, | |
| "partial_rotary_factor": 0.5, | |
| "hidden_act": "swigluoai", | |
| "use_qk_norm": true, | |
| "tie_word_embeddings": false, | |
| "dense_intermediate_size": 12288, | |
| "shared_intermediate_size": 3072, | |
| "num_local_experts": 128, | |
| "num_experts_per_tok": 4, | |
| "n_shared_experts": 1, | |
| "scoring_func": "sigmoid", | |
| "use_routing_bias": true, | |
| "moe_layer_freq": [ | |
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| "swiglu_alpha": 1.702, | |
| "swiglu_limit": 7.0, | |
| "routed_scaling_factor": 2.0, | |
| "sparse_attention_config": { | |
| "use_sparse_attention": true, | |
| "sparse_index_dim": 128, | |
| "sparse_num_index_heads": 4, | |
| "sparse_topk_blocks": 16, | |
| "sparse_block_size": 128, | |
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| "sparse_score_type": "max", | |
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| }, | |
| "architectures": [ | |
| "MiniMaxM3SparseForCausalLM" | |
| ] | |
| }, | |
| "vision_config": { | |
| "hidden_size": 1280, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 32, | |
| "intermediate_size": 5120, | |
| "patch_size": 14, | |
| "image_size": 2016, | |
| "projection_dim": 6144, | |
| "position_embedding_type": "rope", | |
| "rope_mode": "3d", | |
| "rope_theta": 10000.0, | |
| "attention_dropout": 0.0, | |
| "hidden_act": "gelu", | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_channels": 3, | |
| "vocab_size": 32000, | |
| "img_token_compression_config": { | |
| "image_token_compression_method": "patch_merge", | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "vision_segment_max_frames": 4 | |
| }, | |
| "img_token_compression_config": { | |
| "image_token_compression_method": "patch_merge", | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "image_grid_pinpoints": "[(336, 336), (336, 672), (336, 1008), (336, 1344), (336, 1680), (336, 2016), (672, 336), (672, 672), (672, 1008), (672, 1344), (672, 1680), (672, 2016), (1008, 336), (1008, 672), (1008, 1008), (1008, 1344), (1008, 1680), (1008, 2016), (1344, 336), (1344, 672), (1344, 1008), (1344, 1344), (1344, 1680), (1344, 2016), (1680, 336), (1680, 672), (1680, 1008), (1680, 1344), (1680, 1680), (1680, 2016), (2016, 336), (2016, 672), (2016, 1008), (2016, 1344), (2016, 1680), (2016, 2016)]", | |
| "image_seq_length": 576, | |
| "image_token_index": 200025, | |
| "video_token_index": 200026, | |
| "multimodal_projector_bias": true, | |
| "num_reward_heads": 0, | |
| "process_image_mode": "dynamic_res", | |
| "projector_hidden_act": "gelu", | |
| "vision_feature_layer": -1, | |
| "vision_feature_select_strategy": "full", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.52.4", | |
| "projector_hidden_size": 6144, | |
| "quantization_config": { | |
| "quant_method": "mxfp8", | |
| "activation_scheme": "dynamic", | |
| "weight_block_size": [ | |
| 1, | |
| 32 | |
| ], | |
| "ignored_layers": [ | |
| "lm_head", | |
| "model.embed_tokens", | |
| "vision_tower", | |
| "multi_modal_projector", | |
| "patch_merge_mlp", | |
| "language_model.model.layers.10.block_sparse_moe.gate", | |
| "language_model.model.layers.11.block_sparse_moe.gate", | |
| "language_model.model.layers.12.block_sparse_moe.gate", | |
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| ] | |
| } | |
| } |