Instructions to use sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16")
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("sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16")
model = AutoModelForMultimodalLM.from_pretrained("sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16", 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 sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16",
		"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/sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16

SGLang

How to use sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 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 "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16" \
    --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": "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16",
		"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 "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16" \
        --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": "sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16",
		"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 sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 with Docker Model Runner:
```
docker model run hf.co/sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16
```

ThinkingCap-Qwen3.6-27B-NVFP4A16 / README.md

sahilchachra

Upload README.md with huggingface_hub

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license: apache-2.0
base_model: bottlecapai/ThinkingCap-Qwen3.6-27B
base_model_relation: quantized
pipeline_tag: text-generation
tags:
  - nvfp4
  - fp4
  - nvfp4a16
  - compressed-tensors
  - llm-compressor
language:
  - en

ThinkingCap-Qwen3.6-27B-NVFP4A16

NVFP4A16 quantization of bottlecapai/ThinkingCap-Qwen3.6-27B — a token-efficient reasoning finetune of Qwen/Qwen3.6-27B by BottleCap AI (qwen3_5: dense hybrid GatedDeltaNet linear-attention + full-attention over 64 text layers in a 3:1 pattern, plus a vision tower and an MTP head; multimodal image-text-to-text). It matches Qwen3.6-27B answer quality while emitting ~50% fewer thinking tokens on average.

Variant: NVFP4 A16 — 4-bit NVFP4 (FP4 E2M1, group size 16) weights, activations BF16. Native on NVIDIA Blackwell. Quantized by: sahilchachra Tooling: llm-compressor model_free_ptq (data-free, RTN) -> compressed-tensors

This is a quantized derivative. Weights, behavior, and license follow the base model — see the original card for full details, benchmarks, and citation.

What is quantized

Quantized to 4-bit:

full-attention self_attn.{q,k,v,o}_proj
mlp.{gate,up,down}_proj (all text layers)

Kept in BF16: GatedDeltaNet linear_attn (mamba) layers, vision tower (model.visual.*, 27 blocks), MTP head, token embeddings, lm_head, all norms (incl. q_norm / k_norm).

Calibration

Data-free — weight-only (model_free_ptq, round-to-nearest); no calibration data. Weights are quantized by streaming the safetensors from disk.

Prompt template & sampling

This is a reasoning ("thinking") model. Use the Qwen3.6 chat template — ChatML (<|im_start|>role … <|im_end|>) with a <think>…</think> reasoning trace, thinking enabled by default. Apply it via tokenizer.apply_chat_template(messages, add_generation_prompt=True) (or the processor for image inputs); do not hand-format prompts. See the base Qwen/Qwen3.6-27B for full usage details.

Recommended sampling: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0 with thinking on (the base model's recommended sampling, per the original card).

Usage (vLLM)

from vllm import LLM, SamplingParams

# This is a multimodal checkpoint: the vision tower is kept in BF16
# (only the text / MoE weights are 4-bit). vLLM builds the full model.
llm = LLM(
    model="sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16",
    trust_remote_code=True,
)
out = llm.chat(
    [{"role": "user", "content": "Hello!"}],
    SamplingParams(temperature=0.6, top_p=0.95, max_tokens=512),
)
print(out[0].outputs[0].text)

Serving via the CLI, pass the flag directly:

vllm serve sahilchachra/ThinkingCap-Qwen3.6-27B-NVFP4A16 \
    --trust-remote-code \
    --max-model-len 262144 --reasoning-parser qwen3