Instructions to use shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal with Transformers:

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

pipe = pipeline("image-text-to-text", model="shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal")
model = AutoModelForImageTextToText.from_pretrained("shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal")
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

vLLM

How to use shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal",
		"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/shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal

SGLang

How to use shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal 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 "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal" \
    --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": "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal",
		"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 "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal" \
        --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": "shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal",
		"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 shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal with Docker Model Runner:
```
docker model run hf.co/shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-4-31B-Claude-4.6-Opus-thinking-distilled-s7-multimodal

This new release now makes this finetune listed and tuned correctly for multimodality, now ultra capable

Full parameter fine-tune of gemma 4 31b on ~12,000 Claude Opus 4.6 reasoning traces. This is a indigenously made special model

Highlights

~90% token accuracy after 4 epochs
Full parameter SFT, not LoRA
12,000 pure Claude Opus 4.6 traces — consistent reasoning style, no mixed-model data
Native Gemma 4 thinking format — uses standard built-in thinking tokens

Excellent Performance

Reasoning & Knowledge

Benchmark	S7 Score
MMLU Pro	90.3%
GPQA Diamond	89.4%
BigBench Extra Hard	78.9%
MMMLU (Multilingual)	93.7%
HLE (no tools)	20.7%
HLE (with search)	28.1%

Mathematics & Coding

Benchmark	S7 Score
AIME 2026 (no tools)	94.6%
LiveCodeBench v6	84.8%
Codeforces ELO	2279
HumanEval	96.7%
MBPP Plus	94.0%

Multimodal (Vision & Medical)

Benchmark	S7 Score
MMMU Pro	81.5%
MATH-Vision	90.7%
MedXPertQA MM	65.0%

Agentic & Long Context

Benchmark	S7 Score
τ²-bench (Average)	81.5%
τ²-bench (Retail)	91.6%
MRCR v2 (8-needle 128k)	70.4%

Overall Improvement - 6%

Model Specifications

Parameters: 30.7B (Dense)
Architecture: 60 Layers
Context Window: 256K tokens
Vocabulary Size: 262,144
Native Modalities: Text, Image, Video (Frame sequences)

Training Data (~12,000 samples)

Hardware Requirements

Format	VRAM	Device
bf16	~65GB	1x A100/H100 80GB
Q8	~35GB	2x RTX 4090
Q4_K_M	~20GB	RTX 4090
Q3_K_M	~15GB	RTX 4080

IMPORTANT

I WOULD LIKE TO SINCERELY APOLOGISE TO EGANAI AS EARLIER MY TEAM FAILED TO PROPERLY ACCREDIT THEM THIS MODEL HAS BEEN SOURCED FROM THEM AND IS A REUPLOAD

License

MIT

Downloads last month: 50

Safetensors

Model size

31B params

Tensor type

F32

BF16

Model tree for shreyan35/gemma-4-31B-claude-4.6-opus-thinking-distilled-s7-multimodal

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(183)

this model

Quantizations

1 model