Instructions to use MiniMaxAI/MiniMax-M1-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M1-40k with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M1-40k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M1-40k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MiniMaxAI/MiniMax-M1-40k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M1-40k"
# 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-M1-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M1-40k

SGLang

How to use MiniMaxAI/MiniMax-M1-40k 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-M1-40k" \
    --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-M1-40k",
		"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 "MiniMaxAI/MiniMax-M1-40k" \
        --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-M1-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M1-40k with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M1-40k
```

EvalEvalBot commited on Jun 30

Commit

cc61bc7

verified ·

1 Parent(s): 82ba8ac

Update EvalEval source links to flat datastore

Browse files

Updates existing EvalEval Community Evals YAML entries to link to flat datastore aggregate objects pinned to an immutable EEE datastore commit, and normalizes source.name to EvalEval.

No score, dataset, task, or date changes are intended. This updates existing open PR refs only.

Contributor: evaleval

Files changed (1) hide show

.eval_results/mmlu_pro.yaml +1 -1

.eval_results/mmlu_pro.yaml CHANGED Viewed

@@ -3,5 +3,5 @@
     task_id: mmlu_pro
   source:
     name: EvalEval
-    url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/b11a260fe158662bb63b4a144be2b5690615414d/flat/objects/3b/7b/3b7bfaac-ee52-4508-8ecd-676d82e06c1c.json
   value: 81.1

     task_id: mmlu_pro
   source:
     name: EvalEval
+    url: https://huggingface.co/datasets/evaleval/EEE_datastore/blob/192329fb7d6b15b7b0936a1a58ae862aa7e8ba24/flat/objects/3b/7b/3b7bfaac-ee52-4508-8ecd-676d82e06c1c.json
   value: 81.1