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

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 Desktop
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
```

Blackfrost-AI commited on Jul 12

Commit

a08707a

verified ·

1 Parent(s): edbf146

Document Vision (multimodal) capability + vision tags (consistent across the family)

Browse files

Files changed (1) hide show

README.md +9 -0

README.md CHANGED Viewed

@@ -13,6 +13,9 @@ tags:
   - blackfrost
   - the-void
   - bf16
 ---
 ![grok-image-9f997638-c16b-47b7-8ea4-1675a600eafe](https://cdn-uploads.huggingface.co/production/uploads/69a27f2d114e4ac9de4dafc7/1jlDhhHzMbR4pyPR3XDAz.jpeg)
@@ -49,6 +52,12 @@ The model was fine-tuned on **The Void (v4)**, Blackfrost's proprietary distilla
 - **Hardware:** 8× NVIDIA B200, DDP.
 - **Release:** The LoRA adapter was merged back to 16-bit for this release.
 ## Intended Uses
 General-purpose text assistant for reasoning and agentic / tool-use (ReAct-style) tasks.

   - blackfrost
   - the-void
   - bf16
+  - multimodal
+  - vision
+  - vlm
 ---
 ![grok-image-9f997638-c16b-47b7-8ea4-1675a600eafe](https://cdn-uploads.huggingface.co/production/uploads/69a27f2d114e4ac9de4dafc7/1jlDhhHzMbR4pyPR3XDAz.jpeg)
 - **Hardware:** 8× NVIDIA B200, DDP.
 - **Release:** The LoRA adapter was merged back to 16-bit for this release.
+## Vision (multimodal)
+This is a **vision-language model**. It carries a full vision tower inherited from the Qwen3.6-35B vision-language base, so it accepts **images and video** alongside text.
+**Scope of Blackfrost's work:** training here was **text-only**; the vision tower is **inherited unchanged from the base** and was **not tuned or evaluated by Blackfrost**. Multimodal behavior tracks the base model — validate it for your use case.
 ## Intended Uses
 General-purpose text assistant for reasoning and agentic / tool-use (ReAct-style) tasks.