Instructions to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

ChibuUkachi commited on 18 days ago

Commit

d3fa913

1 Parent(s): 61c152a

update unknown

Browse files

Files changed (6) hide show

every_eval_ever_results/aime25.json +3 -3
every_eval_ever_results/gpqa_diamond.json +3 -3
every_eval_ever_results/gsm8k_platinum_cot_llama.json +2 -2
every_eval_ever_results/ifeval.json +2 -2
every_eval_ever_results/math_500.json +3 -3
every_eval_ever_results/mmlu_pro_chat.json +2 -2

every_eval_ever_results/aime25.json CHANGED Viewed

@@ -6,12 +6,12 @@
   "source_metadata": {
     "source_name": "lighteval",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
     "name": "lighteval",
-    "version": "unknown"
   },
   "model_info": {
     "name": "inference-optimization/MiniMax-M2.5.w4a16",
@@ -126,4 +126,4 @@
       }
     }
   ]
-}

   "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": "inference-optimization/MiniMax-M2.5.w4a16",
       }
     }
   ]
+}

every_eval_ever_results/gpqa_diamond.json CHANGED Viewed

@@ -6,12 +6,12 @@
   "source_metadata": {
     "source_name": "lighteval",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
     "name": "lighteval",
-    "version": "unknown"
   },
   "model_info": {
     "name": "inference-optimization/MiniMax-M2.5.w4a16",
@@ -80,4 +80,4 @@
       }
     }
   ]
-}

   "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": "inference-optimization/MiniMax-M2.5.w4a16",
       }
     }
   ]
+}

every_eval_ever_results/gsm8k_platinum_cot_llama.json CHANGED Viewed

@@ -6,7 +6,7 @@
   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
@@ -123,4 +123,4 @@
       }
     }
   ]
-}

   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
+    "source_organization_name": "RedHatAI",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
       }
     }
   ]
+}

every_eval_ever_results/ifeval.json CHANGED Viewed

@@ -6,7 +6,7 @@
   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
@@ -221,4 +221,4 @@
       }
     }
   ]
-}

   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
+    "source_organization_name": "RedHatAI",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
       }
     }
   ]
+}

every_eval_ever_results/math_500.json CHANGED Viewed

@@ -6,12 +6,12 @@
   "source_metadata": {
     "source_name": "lighteval",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
     "name": "lighteval",
-    "version": "unknown"
   },
   "model_info": {
     "name": "inference-optimization/MiniMax-M2.5.w4a16",
@@ -80,4 +80,4 @@
       }
     }
   ]
-}

   "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": "inference-optimization/MiniMax-M2.5.w4a16",
       }
     }
   ]
+}

every_eval_ever_results/mmlu_pro_chat.json CHANGED Viewed

@@ -6,7 +6,7 @@
   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
-    "source_organization_name": "unknown",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
@@ -740,4 +740,4 @@
       }
     }
   ]
-}

   "source_metadata": {
     "source_name": "lm-evaluation-harness",
     "source_type": "evaluation_run",
+    "source_organization_name": "RedHatAI",
     "evaluator_relationship": "third_party"
   },
   "eval_library": {
       }
     }
   ]
+}