Instructions to use amd/gemma-4-31B-it-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/gemma-4-31B-it-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="amd/gemma-4-31B-it-MXFP4")
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("amd/gemma-4-31B-it-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("amd/gemma-4-31B-it-MXFP4", 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 amd/gemma-4-31B-it-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/gemma-4-31B-it-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/gemma-4-31B-it-MXFP4",
		"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/amd/gemma-4-31B-it-MXFP4

SGLang

How to use amd/gemma-4-31B-it-MXFP4 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 "amd/gemma-4-31B-it-MXFP4" \
    --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": "amd/gemma-4-31B-it-MXFP4",
		"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 "amd/gemma-4-31B-it-MXFP4" \
        --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": "amd/gemma-4-31B-it-MXFP4",
		"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 amd/gemma-4-31B-it-MXFP4 with Docker Model Runner:
```
docker model run hf.co/amd/gemma-4-31B-it-MXFP4
```

Model Overview

Base model: google/gemma-4-31B-it
Model architecture: Gemma4ForConditionalGeneration (text-only eval uses Gemma4ForCausalLM override)
Quantizer: AMD Quark
Quantization scheme: mxfp4
Weight quantization: FP4, per-group (MX format)
Activation quantization: FP4, dynamic, per-group (MX format)
Export format: Hugging Face safetensors (quant_method=quark)
Excluded from quantization: vision tower, multimodal projector, lm_head

Runtime Environment

This workflow is intended to run on AMD Instinct MI355.

Start Docker Container

docker run -it -d \
  --name vllm-gemma4-openai \
  --ipc=host \
  --shm-size=64g \
  --network=host \
  --privileged \
  --cap-add=CAP_SYS_ADMIN \
  --device=/dev/kfd \
  --device=/dev/dri \
  --device=/dev/mem \
  --cap-add=SYS_PTRACE \
  --security-opt seccomp=unconfined \
  --entrypoint /bin/bash \
  -v /shareddata:/shareddata -v /mnt:/mnt -v /data:/data -v /home/:/home -v /raid:/raid -v /docker:/docker -v /datasets:/datasets -v /scratch:/scratch -v /group:/group \
vllm/vllm-openai-rocm:gemma4

Quantization

Run quantization inside Docker image vllm/vllm-openai-rocm:gemma4.

Minimal quantization code

from quark.common.profiler import GlobalProfiler
from quark.torch import LLMTemplate, ModelQuantizer

ckpt_path = "google/gemma-4-31B-it"
output_dir = "/path/to/amd/gemma-4-31B-it-mxfp4"

exclude_layers = ["*vision_tower*", "*multi_modal_projector*", "*lm_head"]

if "gemma4" not in LLMTemplate.list_available():
    gemma4_template = LLMTemplate(
        model_type="gemma4",
        kv_layers_name=["*language_model.*k_proj", "*language_model.*v_proj"],
        q_layer_name="*language_model.*q_proj",
        exclude_layers_name=exclude_layers,
    )
    LLMTemplate.register_template(gemma4_template)

template = LLMTemplate.get("gemma4")
quant_config = template.get_config(
    scheme="mxfp4",
    exclude_layers=exclude_layers,
)

profiler = GlobalProfiler(output_path=f"{output_dir}/quark_profile.yaml")
quantizer = ModelQuantizer(quant_config)
quantizer.direct_quantize_checkpoint(
    pretrained_model_path=ckpt_path,
    save_path=output_dir,
)

Evaluation

Reproduction command

CUDA_VISIBLE_DEVICES=6,7 lm_eval run \
  --model vllm \
  --tasks gsm8k \
  --model_args '{"pretrained":"amd/gemma-4-31B-it-mxfp4","tensor_parallel_size":2,"max_model_len":16384,"gpu_memory_utilization":0.90,"max_gen_toks":2048,"trust_remote_code":true,"chat_template":"/scratch/kewang/workspace/vllm/examples/tool_chat_template_gemma4.jinja","reasoning_parser":"gemma4","hf_overrides":{"architectures":["Gemma4ForCausalLM"]}}' \
  --num_fewshot 5 \
  --batch_size auto \
  --apply_chat_template

Accuracy

The following GSM8K results are from local runs in this workspace.

Benchmark	Metric	`google/gemma-4-31B-it`	`amd/gemma-4-31B-it-mxfp4`
gsm8k	exact_match (flexible-extract)	92.65	91.74
gsm8k	exact_match (strict-match)	92.27	91.36

License

This checkpoint is derived from google/gemma-4-31B-it. Please follow the upstream model license and usage terms.

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Safetensors

Model size

17B params

Tensor type

BF16

Model tree for amd/gemma-4-31B-it-MXFP4

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Quantized

(295)

this model