Instructions to use dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B with Transformers:

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

pipe = pipeline("text-generation", model="dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B

SGLang

How to use dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B 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 "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B" \
    --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": "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B",
		"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 "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B" \
        --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": "dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B with Docker Model Runner:
```
docker model run hf.co/dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B
```

dervig commited on Apr 15

Commit

be72489

verified ·

1 Parent(s): 0548ddb

Add deployment pointer for 96 GB Mac

Browse files

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -79,6 +79,10 @@ Final HumanEval and sanity numbers will be added when the current benchmark run
 **5 / 5 pass**. The model is fully usable in production.
 ## Known minor imperfection
 During integrity audit of the 62-layer bias-correction tensor fix, one layer (`layer 0`) had expert keep-indices that differed slightly from the REAP-retained set (86 of 154 positions). The magnitude of the resulting bias mismatch is bounded by the layer-0 bias natural variance (`max |Δ| = 0.75` on values in `[8.06, 8.88]`), so the impact on routing is negligible — confirmed by the 5/5 smoke test above. All other 61 layers are bit-perfect. Full analysis in the [reproducibility log](https://github.com/m51ai/m51Lab-MiniMax-M2.7-REAP/blob/main/docs/research_log.md).

 **5 / 5 pass**. The model is fully usable in production.
+### Deploying on 96 GB Apple Silicon
+The GGUF variants in the [companion repo](https://huggingface.co/dervig/m51Lab-MiniMax-M2.7-REAP-139B-A10B-GGUF) are the practical choice for 96 GB Mac Studio / M4 Max. That card contains an explicit memory & context sizing guide — **note that at long context, KV cache quantization (`--cache-type-k q8_0`) is essential for this architecture** (~0.25 GB of FP16 KV cache per 1K tokens across 62 layers).
 ## Known minor imperfection
 During integrity audit of the 62-layer bias-correction tensor fix, one layer (`layer 0`) had expert keep-indices that differed slightly from the REAP-retained set (86 of 154 positions). The magnitude of the resulting bias mismatch is bounded by the layer-0 bias natural variance (`max |Δ| = 0.75` on values in `[8.06, 8.88]`), so the impact on routing is negligible — confirmed by the 5/5 smoke test above. All other 61 layers are bit-perfect. Full analysis in the [reproducibility log](https://github.com/m51ai/m51Lab-MiniMax-M2.7-REAP/blob/main/docs/research_log.md).