Instructions to use BirdToast/TestWL-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BirdToast/TestWL-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="BirdToast/TestWL-2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("BirdToast/TestWL-2")
model = AutoModelForMultimodalLM.from_pretrained("BirdToast/TestWL-2", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BirdToast/TestWL-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BirdToast/TestWL-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdToast/TestWL-2",
		"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/BirdToast/TestWL-2

SGLang

How to use BirdToast/TestWL-2 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 "BirdToast/TestWL-2" \
    --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": "BirdToast/TestWL-2",
		"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 "BirdToast/TestWL-2" \
        --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": "BirdToast/TestWL-2",
		"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 BirdToast/TestWL-2 with Docker Model Runner:
```
docker model run hf.co/BirdToast/TestWL-2
```

TestWL-2

File size: 1,477 Bytes

---
base_model:
- llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic
- LatitudeGames/Equinox-31B
- ReadyArt/gemma-4-31B-it-scotoma-2
library_name: transformers
tags:
- mergekit
- mergekitty
- merge

---
# fm-test-merge-2-v2

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

## Merge Details
### Merge Method

This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method using [llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic](https://huggingface.co/llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic) as a base.

### Models Merged

The following models were included in the merge:
* [LatitudeGames/Equinox-31B](https://huggingface.co/LatitudeGames/Equinox-31B)
* [ReadyArt/gemma-4-31B-it-scotoma-2](https://huggingface.co/ReadyArt/gemma-4-31B-it-scotoma-2)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
architecture: Gemma4ForConditionalGeneration
base_model: llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic
models:
  - model: llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic
    parameters:
      weight: [1, 0.1, 0.1, 0.1]
  - model: ReadyArt/gemma-4-31B-it-scotoma-2
    parameters:
      weight: [0, 0.8, 0.8, 0.9]
  - model: LatitudeGames/Equinox-31B
    parameters:
      weight: [0, 0.1, 0.1, 0]
merge_method: linear
dtype: bfloat16
out_dtype: bfloat16
chat_template: auto

```