Instructions to use helenk/gemma-4-E2B-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use helenk/gemma-4-E2B-finetune with Transformers:

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

pipe = pipeline("text-generation", model="helenk/gemma-4-E2B-finetune")
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("helenk/gemma-4-E2B-finetune")
model = AutoModelForMultimodalLM.from_pretrained("helenk/gemma-4-E2B-finetune", 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]:]))

PEFT
How to use helenk/gemma-4-E2B-finetune with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use helenk/gemma-4-E2B-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "helenk/gemma-4-E2B-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "helenk/gemma-4-E2B-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/helenk/gemma-4-E2B-finetune

SGLang

How to use helenk/gemma-4-E2B-finetune 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 "helenk/gemma-4-E2B-finetune" \
    --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": "helenk/gemma-4-E2B-finetune",
		"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 "helenk/gemma-4-E2B-finetune" \
        --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": "helenk/gemma-4-E2B-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use helenk/gemma-4-E2B-finetune with Docker Model Runner:
```
docker model run hf.co/helenk/gemma-4-E2B-finetune
```

gemma-4-E2B-finetune / .gitattributes

helenk

Upload E2B merged fp16 fine-tune (2026-05-09 retrain) — base unsloth/gemma-4-E2B-it + helenk/gemma-4-E2B-lora, peft merge_and_unload, fp16 safetensors. Sibling of helenk/gemma-4-E4B-finetune; smaller variant for lower-memory targets.

629df18 verified 4 months ago

Raw

History Blame Contribute Delete

1.57 kB

	*.7z filter=lfs diff=lfs merge=lfs -text
	*.arrow filter=lfs diff=lfs merge=lfs -text
	*.bin filter=lfs diff=lfs merge=lfs -text
	*.bz2 filter=lfs diff=lfs merge=lfs -text
	*.ckpt filter=lfs diff=lfs merge=lfs -text
	*.ftz filter=lfs diff=lfs merge=lfs -text
	*.gz filter=lfs diff=lfs merge=lfs -text
	*.h5 filter=lfs diff=lfs merge=lfs -text
	*.joblib filter=lfs diff=lfs merge=lfs -text
	.lfs. filter=lfs diff=lfs merge=lfs -text
	*.mlmodel filter=lfs diff=lfs merge=lfs -text
	*.model filter=lfs diff=lfs merge=lfs -text
	*.msgpack filter=lfs diff=lfs merge=lfs -text
	*.npy filter=lfs diff=lfs merge=lfs -text
	*.npz filter=lfs diff=lfs merge=lfs -text
	*.onnx filter=lfs diff=lfs merge=lfs -text
	*.ot filter=lfs diff=lfs merge=lfs -text
	*.parquet filter=lfs diff=lfs merge=lfs -text
	*.pb filter=lfs diff=lfs merge=lfs -text
	*.pickle filter=lfs diff=lfs merge=lfs -text
	*.pkl filter=lfs diff=lfs merge=lfs -text
	*.pt filter=lfs diff=lfs merge=lfs -text
	*.pth filter=lfs diff=lfs merge=lfs -text
	*.rar filter=lfs diff=lfs merge=lfs -text
	*.safetensors filter=lfs diff=lfs merge=lfs -text
	saved_model/*/ filter=lfs diff=lfs merge=lfs -text
	.tar. filter=lfs diff=lfs merge=lfs -text
	*.tar filter=lfs diff=lfs merge=lfs -text
	*.tflite filter=lfs diff=lfs merge=lfs -text
	*.tgz filter=lfs diff=lfs merge=lfs -text
	*.wasm filter=lfs diff=lfs merge=lfs -text
	*.xz filter=lfs diff=lfs merge=lfs -text
	*.zip filter=lfs diff=lfs merge=lfs -text
	*.zst filter=lfs diff=lfs merge=lfs -text
	tfevents filter=lfs diff=lfs merge=lfs -text
	tokenizer.json filter=lfs diff=lfs merge=lfs -text