Text Generation
Transformers
Safetensors
llama
eagle3
speculative-decoding
draft-model
vllm
torchspec
minimax
text-generation-inference
Instructions to use Inferact/MiniMax-M3-EAGLE3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Inferact/MiniMax-M3-EAGLE3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Inferact/MiniMax-M3-EAGLE3")# Load model directly from transformers import AutoTokenizer, LlamaForCausalLMEagle3 tokenizer = AutoTokenizer.from_pretrained("Inferact/MiniMax-M3-EAGLE3") model = LlamaForCausalLMEagle3.from_pretrained("Inferact/MiniMax-M3-EAGLE3") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Inferact/MiniMax-M3-EAGLE3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Inferact/MiniMax-M3-EAGLE3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Inferact/MiniMax-M3-EAGLE3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Inferact/MiniMax-M3-EAGLE3
- SGLang
How to use Inferact/MiniMax-M3-EAGLE3 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 "Inferact/MiniMax-M3-EAGLE3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Inferact/MiniMax-M3-EAGLE3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Inferact/MiniMax-M3-EAGLE3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Inferact/MiniMax-M3-EAGLE3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Inferact/MiniMax-M3-EAGLE3 with Docker Model Runner:
docker model run hf.co/Inferact/MiniMax-M3-EAGLE3
| { | |
| "architectures": [ | |
| "LlamaForCausalLMEagle3" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "fc_norm": true, | |
| "norm_output": true, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 6144, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 18432, | |
| "max_position_embeddings": 1048576, | |
| "model_type": "llama", | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 1, | |
| "num_key_value_heads": 64, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 5000000, | |
| "sliding_window": null, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.51.0", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 200064, | |
| "draft_vocab_size": 200064, | |
| "_torchspec_version": "0.1.0" | |
| } |