Instructions to use BirdToast/TestWL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BirdToast/TestWL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="BirdToast/TestWL") 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("BirdToast/TestWL") model = AutoModelForMultimodalLM.from_pretrained("BirdToast/TestWL", 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 BirdToast/TestWL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BirdToast/TestWL" # 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", "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
- SGLang
How to use BirdToast/TestWL 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" \ --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", "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" \ --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", "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 with Docker Model Runner:
docker model run hf.co/BirdToast/TestWL
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Download README.md from BirdToast/TestWL: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/BirdToast/TestWL/resolve/main/README.md
- Command line
-
hf download hf://BirdToast/TestWL/README.md
-
curl -L -o README.md https://huggingface.co/BirdToast/TestWL/resolve/main/README.md
1.22 kB
| base_model: | |
| - LatitudeGames/Equinox-31B | |
| - llmfan46/Gemma-4-Garnet-V2-31B-it-ultra-uncensored-heretic | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - mergekitty | |
| - merge | |
| # test-fm-merge | |
| 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) | |
| ### 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 | |
| - model: LatitudeGames/Equinox-31B | |
| parameters: | |
| weight: 0.1 | |
| merge_method: linear | |
| dtype: bfloat16 | |
| out_dtype: bfloat16 | |
| chat_template: auto | |
| ``` | |