Text Generation
Transformers
Safetensors
minimax_m2
neuralmagic
redhat
llmcompressor
quantized
INT4
conversational
custom_code
compressed-tensors
Instructions to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/MiniMax-M2.5-quantized.w4a16", 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.w4a16", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("RedHatAI/MiniMax-M2.5-quantized.w4a16", 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.w4a16 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.w4a16" # 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.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w4a16
- SGLang
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 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.w4a16" \ --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.w4a16", "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.w4a16" \ --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.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with Docker Model Runner:
docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w4a16
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| "evaluation_id": "ifeval/RedHatAI/MiniMax-M2.5-quantized.w4a16/1777302591.528493", | |
| "evaluation_timestamp": "1776886012", | |
| "retrieved_timestamp": "1777302591.528493", | |
| "source_metadata": { | |
| "source_name": "lm-evaluation-harness", | |
| "source_type": "evaluation_run", | |
| "source_organization_name": "RedHatAI", | |
| "evaluator_relationship": "third_party" | |
| }, | |
| "eval_library": { | |
| "name": "lm_eval", | |
| "version": "0.4.12.dev0" | |
| }, | |
| "model_info": { | |
| "name": "RedHatAI/MiniMax-M2.5-quantized.w4a16", | |
| "id": "RedHatAI/MiniMax-M2.5-quantized.w4a16", | |
| "developer": "RedHatAI", | |
| "additional_details": { | |
| "model_args": "{'model': 'RedHatAI/MiniMax-M2.5-quantized.w4a16', 'max_length': 196608, 'base_url': 'http://0.0.0.0:8000/v1/chat/completions', 'num_concurrent': 128, 'max_retries': 3, 'tokenized_requests': False, 'tokenizer_backend': None, 'timeout': 2400}", | |
| "seed": "1234", | |
| "num_seeds_merged": "3" | |
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| "evaluation_results": [ | |
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| "evaluation_timestamp": "1776888058", | |
| "metric_config": { | |
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| "score_type": "continuous", | |
| "min_score": 0.0, | |
| "max_score": 1.0 | |
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| "score_details": { | |
| "score": 0.8558225508317929, | |
| "details": { | |
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| "seed_values": "[1234, 4158, 42]" | |
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| "method": "across_seeds" | |
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| "generation_config": { | |
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| { | |
| "evaluation_name": "ifeval", | |
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| "dataset_name": "ifeval", | |
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| "hf_repo": "google/IFEval", | |
| "hf_split": "train" | |
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| "evaluation_timestamp": "1776888058", | |
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| { | |
| "evaluation_name": "ifeval", | |
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