Instructions to use FINAL-Bench/Darwin-35B-A3B-Opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-35B-A3B-Opus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="FINAL-Bench/Darwin-35B-A3B-Opus") 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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-35B-A3B-Opus") model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-35B-A3B-Opus") 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
- vLLM
How to use FINAL-Bench/Darwin-35B-A3B-Opus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-35B-A3B-Opus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-35B-A3B-Opus", "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/FINAL-Bench/Darwin-35B-A3B-Opus
- SGLang
How to use FINAL-Bench/Darwin-35B-A3B-Opus 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 "FINAL-Bench/Darwin-35B-A3B-Opus" \ --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": "FINAL-Bench/Darwin-35B-A3B-Opus", "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 "FINAL-Bench/Darwin-35B-A3B-Opus" \ --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": "FINAL-Bench/Darwin-35B-A3B-Opus", "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 FINAL-Bench/Darwin-35B-A3B-Opus with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-35B-A3B-Opus
27B-v2 Jackrong Suffer from dead vision experts?
if I use 27B Jackrong v2 model with the original vision weights from qwen will there still be a dead experts issue unless this type of merge is performed on the dense model or is only MoE qwen model affected?
No, dead experts is a MoE-only issue. Dense models don't have experts, so there's nothing to "die."
The Jackrong 27B is a dense model — all 27B parameters activate on every forward pass. Dead experts only occur in MoE architectures where the router fails to route tokens to certain experts.
However, if you're trying to graft Qwen's original vision weights onto the Jackrong 27B (which was fine-tuned as text-only), you may face a vision-text alignment issue — not dead experts, but the vision encoder projections may not align well with the SFT-modified text layers. A short vision QA fine-tuning pass should fix that.
Or, you can simply use our Darwin-35B-A3B-Opus directly — it's built on Qwen3.5-35B-A3B which has native vision support out of the box. No need to graft anything.