Text Generation
Transformers
Safetensors
qwen3_5_moe
image-text-to-text
Mixture of Experts
mixture-of-experts
unsloth
sft
lora
bf16
multimodal
vision
vlm
conversational
Instructions to use Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16") 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("Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16") model = AutoModelForMultimodalLM.from_pretrained("Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16", 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 Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16
- SGLang
How to use Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 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 "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16" \ --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": "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16", "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 "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16" \ --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": "Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16", max_seq_length=2048, ) - Docker Model Runner
How to use Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16 with Docker Model Runner:
docker model run hf.co/Blackfrost-AI/PINQWEN-3.6-35B-CLEAN-BF16
- Xet hash:
- 90af691084ce44447602e69f58e21a8f5cae1461bddb9b37adcf7cf9c2f7e1b9
- Size of remote file:
- 2.23 GB
- SHA256:
- 5702a861dd4d8bbf47c4bd7b8ea0933f0c0c55d02ee99800127cce76e7c9853d
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