Instructions to use zelk12/Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zelk12/Test with Transformers:

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

pipe = pipeline("image-text-to-text", model="zelk12/Test")
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("zelk12/Test")
model = AutoModelForMultimodalLM.from_pretrained("zelk12/Test", 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 zelk12/Test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zelk12/Test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zelk12/Test",
		"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/zelk12/Test

SGLang

How to use zelk12/Test 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 "zelk12/Test" \
    --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": "zelk12/Test",
		"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 "zelk12/Test" \
        --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": "zelk12/Test",
		"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 zelk12/Test with Docker Model Runner:
```
docker model run hf.co/zelk12/Test
```

Test

File size: 1,425 Bytes

---
base_model:
- WWTCyberLab/gemma-4-E2B-it-abliterated
- google/gemma-4-E2B-it
library_name: transformers
tags:
- mergekit
- merge
- gemma
- gemma4
- gemma4_E2B

---
# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method using [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) as a base.

### Models Merged

The following models were included in the merge:
* [WWTCyberLab/gemma-4-E2B-it-abliterated](https://huggingface.co/WWTCyberLab/gemma-4-E2B-it-abliterated)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: google/gemma-4-E2B-it
    parameters:
      density: 0.5
      weight: 0.32

  - model: WWTCyberLab/gemma-4-E2B-it-abliterated
    parameters:
      density: 0.5
      weight: 0.68

merge_method: linear
base_model: google/gemma-4-E2B-it
parameters:
  normalize: true
dtype: bfloat16
tokenizer_source: base

```
EN

The files used for merging are located at the following address: https://huggingface.co/zelk12/Mergekit_Gemma-4-E2B

___
___

RU

Файлы, которые использовались для объединения находятся по следующему адресу: https://huggingface.co/zelk12/Mergekit_Gemma-4-E2B