Instructions to use thoughtworks/MiniMax-M2.5-Eagle3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thoughtworks/MiniMax-M2.5-Eagle3 with Transformers:

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

pipe = pipeline("text-generation", model="thoughtworks/MiniMax-M2.5-Eagle3")

# Load model directly
from transformers import AutoTokenizer, LlamaForCausalLMEagle3

tokenizer = AutoTokenizer.from_pretrained("thoughtworks/MiniMax-M2.5-Eagle3")
model = LlamaForCausalLMEagle3.from_pretrained("thoughtworks/MiniMax-M2.5-Eagle3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thoughtworks/MiniMax-M2.5-Eagle3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thoughtworks/MiniMax-M2.5-Eagle3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thoughtworks/MiniMax-M2.5-Eagle3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thoughtworks/MiniMax-M2.5-Eagle3

SGLang

How to use thoughtworks/MiniMax-M2.5-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 "thoughtworks/MiniMax-M2.5-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": "thoughtworks/MiniMax-M2.5-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 "thoughtworks/MiniMax-M2.5-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": "thoughtworks/MiniMax-M2.5-Eagle3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thoughtworks/MiniMax-M2.5-Eagle3 with Docker Model Runner:
```
docker model run hf.co/thoughtworks/MiniMax-M2.5-Eagle3
```

MiniMax-M2.5-Eagle3 / config.json

lujangusface

Upload config.json with huggingface_hub

42b815f verified 3 months ago

Raw

History Blame Contribute Delete

874 Bytes

	{
	"architectures": [
	"LlamaForCausalLMEagle3"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 1,
	"draft_vocab_size": 32000,
	"dtype": "bfloat16",
	"eagle_config": {
	"eagle_aux_hidden_state_layer_ids": [
	1,
	30,
	58
	],
	"use_aux_hidden_state": true
	},
	"eos_token_id": 2,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 3072,
	"initializer_range": 0.02,
	"intermediate_size": 8192,
	"max_position_embeddings": 196608,
	"mlp_bias": false,
	"model_type": "llama",
	"num_attention_heads": 24,
	"num_hidden_layers": 1,
	"num_key_value_heads": 8,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 5000000.0,
	"target_hidden_size": 3072,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.1",
	"use_cache": true,
	"vocab_size": 200064
	}