Instructions to use nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37 with Transformers:

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

pipe = pipeline("image-text-to-text", model="nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37")
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("nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37")
model = AutoModelForMultimodalLM.from_pretrained("nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37", 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 nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37",
		"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/nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37

SGLang

How to use nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37 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 "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37" \
    --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": "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37",
		"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 "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37" \
        --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": "nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37",
		"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 nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37 with Docker Model Runner:
```
docker model run hf.co/nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37
```

LiveGraph Gemma 4 31B SFT — linear trace, seed 37

This is a full-parameter BF16 fine-tune of google/gemma-4-31B for the LiveGraph synthetic Boolean-propagation task. The training rendering uses a linearized execution trace. This checkpoint is one seed of a matched representation study; it is not an instruction-tuned general-purpose assistant.

Model details

Architecture: Gemma4ForConditionalGeneration
Checkpoint index metadata: 32,682,372,656 parameters and 62,546,177,752 serialized bytes in two BF16 safetensors shards
Parameter accounting: the separate GRPO runtime manifest reported 31,395,515,952 parameters after adding 122,429,440 LoRA parameters. The full-checkpoint index retains the upstream base index's 32,682,372,656 logical-parameter convention. Runtime unique-module counts and index metadata can differ because of weight tying and language, embedding, and vision components; the serialized payload and weight map were verified directly.
Training: 480 unique synthetic items, balanced TRUE/FALSE labels, 8 epochs
Objective: completion-only supervised fine-tuning
Context limit used in training: 4,096 tokens; no training row was truncated
Learning rate / global batch size: 1e-5 / 32
Seed / final training loss: 37 / 0.736526
Base config SHA-256: e967dd38bc5cfd38bd09a995a7bf4a754075df2b46aba68f7fbb5a791e6d8dd1
Training-data SHA-256: d5bdea5629c84bbd7fec51f5969e06612b27b817bc302a15045fe7a46e2eb792

Evaluation

Strict exact-label accuracy was measured on a frozen 320-item set spanning depths 1–32, under native thinking, max_tokens=8192, and vLLM FP8 serving. Unparseable completions count as incorrect.

Evaluation rendering	Strict accuracy	Parse rate
Typed graph	51.25%	100.00%
Linear trace	50.00%	100.00%

These are protocol-conditional research measurements, not evidence of broad capability improvement. The study used only three seeds and a fixed execution order, so causal claims about representation are not supported.

Use

from transformers import AutoModelForMultimodalLM, AutoProcessor

processor = AutoProcessor.from_pretrained(".")
model = AutoModelForMultimodalLM.from_pretrained(
    ".", dtype="auto", device_map="auto"
)

Use the supplied chat template and the LiveGraph task prompt format. The model was evaluated only for short TRUE/FALSE completions on synthetic graph instances.

Limitations and risks

The data are synthetic and English-only. Accuracy is near chance on this evaluation, and results may change with decoding, quantization, prompt formatting, or graph distribution. The base model's multimodal and safety properties were not re-evaluated after fine-tuning. Do not use this checkpoint for consequential decisions.

License and provenance

The checkpoint is a modified derivative of Google DeepMind's google/gemma-4-31B, whose local model card declares Apache License 2.0. The full license is included in LICENSE; modification and attribution information is in NOTICE. File hashes are listed in SHA256SUMS.

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Safetensors

Model size

33B params

Tensor type

BF16

Model tree for nutshells3/livegraph-gemma4-31b-sft-linear-trace-seed-37

Base model

google/gemma-4-31B

Finetuned

(74)

this model