Instructions to use RedHatAI/MiniMax-M2.5-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/MiniMax-M2.5-quantized.w8a8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/MiniMax-M2.5-quantized.w8a8", 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("RedHatAI/MiniMax-M2.5-quantized.w8a8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("RedHatAI/MiniMax-M2.5-quantized.w8a8", 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

vLLM

How to use RedHatAI/MiniMax-M2.5-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/MiniMax-M2.5-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/MiniMax-M2.5-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w8a8

SGLang

How to use RedHatAI/MiniMax-M2.5-quantized.w8a8 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 "RedHatAI/MiniMax-M2.5-quantized.w8a8" \
    --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": "RedHatAI/MiniMax-M2.5-quantized.w8a8",
		"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 "RedHatAI/MiniMax-M2.5-quantized.w8a8" \
        --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": "RedHatAI/MiniMax-M2.5-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/MiniMax-M2.5-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w8a8
```

MiniMax-M2.5-quantized.w8a8 / every_eval_ever /aime25.json

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	{
	"schema_version": "0.2.2",
	"evaluation_id": "aime25/RedHatAI/MiniMax-M2.5-quantized.w8a8/1777568692.163912",
	"evaluation_timestamp": "7718166",
	"retrieved_timestamp": "1777568692.163912",
	"source_metadata": {
	"source_name": "lighteval",
	"source_type": "evaluation_run",
	"source_organization_name": "RedHatAI",
	"evaluator_relationship": "third_party"
	},
	"eval_library": {
	"name": "lighteval",
	"version": "v0.13.0"
	},
	"model_info": {
	"name": "RedHatAI/MiniMax-M2.5-quantized.w8a8",
	"id": "RedHatAI/MiniMax-M2.5-quantized.w8a8",
	"developer": "RedHatAI",
	"inference_engine": {
	"name": "vllm"
	},
	"additional_details": {
	"provider": "hosted_vllm",
	"base_url": "http://0.0.0.0:8000/v1",
	"concurrent_requests": "8",
	"verbose": "False",
	"api_max_retry": "8",
	"api_retry_sleep": "1.0",
	"api_retry_multiplier": "2.0",
	"timeout": "2400.0",
	"num_seeds_merged": "8"
	}
	},
	"evaluation_results": [
	{
	"evaluation_name": "aime25",
	"source_data": {
	"dataset_name": "aime25",
	"source_type": "hf_dataset",
	"hf_repo": "yentinglin/aime_2025",
	"hf_split": "train"
	},
	"evaluation_timestamp": "7721195",
	"metric_config": {
	"evaluation_description": "pass@k:k=1&n=1",
	"lower_is_better": false,
	"score_type": "continuous",
	"min_score": 0.0,
	"max_score": 1.0
	},
	"score_details": {
	"score": 0.8833333333333333,
	"details": {
	"seed_scores": "[0.9, 0.9, 0.9, 0.9, 0.8666666666666667, 0.8333333333333334, 0.8666666666666667, 0.9]",
	"seed_values": "[1234, 1356, 3344, 4158, 42, 5322, 5678, 9843]"
	},
	"uncertainty": {
	"standard_error": {
	"value": 0.008908708063747477,
	"method": "across_seeds"
	},
	"num_samples": 8
	}
	},
	"generation_config": {
	"generation_args": {
	"temperature": 1.0,
	"top_p": 0.95,
	"top_k": 40.0,
	"max_tokens": 64000,
	"max_attempts": 1
	},
	"additional_details": {
	"repetition_penalty": "1.0",
	"presence_penalty": "1.5",
	"seed": "1234",
	"min_p": "0.0"
	}
	}
	},
	{
	"evaluation_name": "aime25",
	"source_data": {
	"dataset_name": "aime25",
	"source_type": "hf_dataset",
	"hf_repo": "yentinglin/aime_2025",
	"hf_split": "train"
	},
	"evaluation_timestamp": "7721195",
	"metric_config": {
	"evaluation_description": "avg@n:n=1",
	"lower_is_better": false,
	"score_type": "continuous",
	"min_score": 0.0,
	"max_score": 1.0
	},
	"score_details": {
	"score": 0.8833333333333333,
	"details": {
	"seed_scores": "[0.9, 0.9, 0.9, 0.9, 0.8666666666666667, 0.8333333333333334, 0.8666666666666667, 0.9]",
	"seed_values": "[1234, 1356, 3344, 4158, 42, 5322, 5678, 9843]"
	},
	"uncertainty": {
	"standard_error": {
	"value": 0.008908708063747477,
	"method": "across_seeds"
	},
	"num_samples": 8
	}
	},
	"generation_config": {
	"generation_args": {
	"temperature": 1.0,
	"top_p": 0.95,
	"top_k": 40.0,
	"max_tokens": 64000,
	"max_attempts": 1
	},
	"additional_details": {
	"repetition_penalty": "1.0",
	"presence_penalty": "1.5",
	"seed": "1234",
	"min_p": "0.0"
	}
	}
	}
	]
	}