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

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
```

lujangusface commited on Apr 12

Commit

29cdd29

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@@ -115,7 +115,9 @@ EAGLE3 trains a single-layer draft head that predicts the next token using hidde
 *The released model was fine-tuned for 6 additional epochs on 20K regenerated samples from the target model. The fine-tuned accuracy is expected to be equal or higher than these base values.*
-### Inference Benchmarks (B=1, temp=0, FP8, TP=4)
 | Dataset | Baseline (tok/s) | EAGLE3 (tok/s) | Speedup |
 |---------|-----------------|----------------|---------|
@@ -124,7 +126,18 @@ EAGLE3 trains a single-layer draft head that predicts the next token using hidde
 | SWEBench-Verified | 109.6 | 191.8 | **1.75x** |
 | Aider | 109.9 | 186.8 | **1.70x** |
-*Config: steps=3, topk=4, draft_tokens=8. All datasets at temp=0 on 8x H200 (TP=4).*
 ## Model Architecture

 *The released model was fine-tuned for 6 additional epochs on 20K regenerated samples from the target model. The fine-tuned accuracy is expected to be equal or higher than these base values.*
+### Inference Benchmarks (B=1, temp=0, TP=4)
+**With draft_tokens=8 (best B=1 config)**:
 | Dataset | Baseline (tok/s) | EAGLE3 (tok/s) | Speedup |
 |---------|-----------------|----------------|---------|
 | SWEBench-Verified | 109.6 | 191.8 | **1.75x** |
 | Aider | 109.9 | 186.8 | **1.70x** |
+*Config: steps=3, topk=4, draft_tokens=8. 8x H200 (TP=4).*
+**With draft_tokens=6 (standard config, verified 2026-04-12)**:
+| Dataset | Baseline (tok/s) | EAGLE3 (tok/s) | Speedup |
+|---------|-----------------|----------------|---------|
+| HumanEval | 109.6 | 158.0 | **1.44x** |
+| Terminal-Bench | 108.9 | 150.2 | **1.38x** |
+| MT-Bench | 109.0 | 143.6 | **1.32x** |
+| SWEBench-Verified | 109.1 | 116.5 | **1.07x** |
+*Config: steps=3, topk=4, draft_tokens=6. 4x H200 (TP=4). Server-side Prometheus metrics.*
 ## Model Architecture