Instructions to use RedHatAI/gemma-4-31B-it-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/gemma-4-31B-it-FP8-dynamic with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/gemma-4-31B-it-FP8-dynamic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("RedHatAI/gemma-4-31B-it-FP8-dynamic")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/gemma-4-31B-it-FP8-dynamic", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/gemma-4-31B-it-FP8-dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/gemma-4-31B-it-FP8-dynamic"
# 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/gemma-4-31B-it-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/gemma-4-31B-it-FP8-dynamic

SGLang

How to use RedHatAI/gemma-4-31B-it-FP8-dynamic 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/gemma-4-31B-it-FP8-dynamic" \
    --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/gemma-4-31B-it-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/gemma-4-31B-it-FP8-dynamic" \
        --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/gemma-4-31B-it-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/gemma-4-31B-it-FP8-dynamic with Docker Model Runner:
```
docker model run hf.co/RedHatAI/gemma-4-31B-it-FP8-dynamic
```

alexmarques commited on Jun 18

Commit

fe480b5

verified ·

1 Parent(s): ef901b9

Add eval results: aime25.json

Browse files

Files changed (1) hide show

every_eval_ever/aime25.json +125 -0

every_eval_ever/aime25.json ADDED Viewed

	@@ -0,0 +1,125 @@

+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "aime25|0/RedHatAI/gemma-4-31B-it-FP8-Dynamic/1781646637.455555",
+  "evaluation_timestamp": "352364",
+  "retrieved_timestamp": "1781646637.455555",
+  "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/gemma-4-31B-it-FP8-Dynamic",
+    "id": "RedHatAI/gemma-4-31B-it-FP8-Dynamic",
+    "developer": "RedHatAI",
+    "inference_engine": {
+      "name": "vllm"
+    },
+    "additional_details": {
+      "provider": "hosted_vllm",
+      "base_url": "http://127.0.0.1:8000/v1",
+      "concurrent_requests": "128",
+      "verbose": "False",
+      "api_max_retry": "8",
+      "api_retry_sleep": "1.0",
+      "api_retry_multiplier": "2.0",
+      "timeout": "3600.0",
+      "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"
+      },
+      "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": 0.6625,
+        "details": {
+          "seed_scores": "[0.6666666666666666, 0.7333333333333333, 0.6333333333333333, 0.6, 0.6, 0.7666666666666667, 0.7333333333333333, 0.5666666666666667]",
+          "evaluation_timestamps": "[352364, 352553, 352698, 352901, 353074, 353294, 353551, 353683]",
+          "seed_values": "[1234, 2345, 3456, 4567, 5678, 6789, 7890, 8901]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.02630521404045756,
+            "method": "across_seeds"
+          },
+          "num_samples": 8
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 64.0,
+          "max_tokens": 65536,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "seed": "1234",
+          "num_fewshot": "0"
+        }
+      }
+    },
+    {
+      "evaluation_name": "aime25",
+      "source_data": {
+        "dataset_name": "aime25",
+        "source_type": "hf_dataset",
+        "hf_repo": "yentinglin/aime_2025",
+        "hf_split": "train"
+      },
+      "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": 0.6625,
+        "details": {
+          "seed_scores": "[0.6666666666666666, 0.7333333333333333, 0.6333333333333333, 0.6, 0.6, 0.7666666666666667, 0.7333333333333333, 0.5666666666666667]",
+          "evaluation_timestamps": "[352364, 352553, 352698, 352901, 353074, 353294, 353551, 353683]",
+          "seed_values": "[1234, 2345, 3456, 4567, 5678, 6789, 7890, 8901]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.02630521404045756,
+            "method": "across_seeds"
+          },
+          "num_samples": 8
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 64.0,
+          "max_tokens": 65536,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "seed": "1234",
+          "num_fewshot": "0"
+        }
+      }
+    }
+  ]
+}