Instructions to use maci0/Qwopus3.6-27B-Coder-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maci0/Qwopus3.6-27B-Coder-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="maci0/Qwopus3.6-27B-Coder-NVFP4")
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("maci0/Qwopus3.6-27B-Coder-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("maci0/Qwopus3.6-27B-Coder-NVFP4", 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 maci0/Qwopus3.6-27B-Coder-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maci0/Qwopus3.6-27B-Coder-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maci0/Qwopus3.6-27B-Coder-NVFP4",
		"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/maci0/Qwopus3.6-27B-Coder-NVFP4

SGLang

How to use maci0/Qwopus3.6-27B-Coder-NVFP4 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 "maci0/Qwopus3.6-27B-Coder-NVFP4" \
    --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": "maci0/Qwopus3.6-27B-Coder-NVFP4",
		"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 "maci0/Qwopus3.6-27B-Coder-NVFP4" \
        --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": "maci0/Qwopus3.6-27B-Coder-NVFP4",
		"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 maci0/Qwopus3.6-27B-Coder-NVFP4 with Docker Model Runner:
```
docker model run hf.co/maci0/Qwopus3.6-27B-Coder-NVFP4
```

maci0 commited on 21 days ago

Commit

1d6345e

verified ·

1 Parent(s): a737fcb

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Browse files

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README.md CHANGED Viewed

@@ -23,7 +23,7 @@ tags:
   - coder
 ---
-<div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;max-width:760px;border:1px solid #cec9ba;background:#f2efe6;color:#14130f;padding:24px 26px;border-radius:2px;margin-bottom:26px;"><div style="display:flex;justify-content:space-between;align-items:center;gap:10px 14px;flex-wrap:wrap;"><span style="display:inline-flex;align-items:center;gap:9px;"><svg width="18" height="18" viewBox="0 0 24 24" aria-hidden="true" style="flex-shrink:0;"><path d="M4 4 H16 L20 8 V20 H4 Z" fill="none" stroke="#6f6b60" stroke-width="1.25"/><path d="M16 4 V8 H20" fill="none" stroke="#6f6b60" stroke-width="1.25"/><circle cx="9" cy="13" r="1" fill="#6f6b60"/><circle cx="13" cy="15" r="1" fill="#6f6b60"/></svg><span style="font-family:ui-monospace,SFMono-Regular,Menlo,monospace;font-size:12px;font-weight:700;letter-spacing:0.02em;">RQ-27B-CODER</span></span></div><div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;font-size:26px;font-weight:800;letter-spacing:-0.02em;line-height:1.15;margin:18px 0 8px;color:#14130f;">Qwopus3.6-27B-Coder · NVFP4</div><div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;font-size:14.5px;line-height:1.5;color:#4a4740;margin-bottom:18px;">27B agentic coder VL · tool-calling · thinking-mode reasoning (censored reference).</div><table style="width:100%;border-collapse:collapse;border:0;margin:2px 0 0;font-variant-numeric:tabular-nums;"><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-top:1.5px solid #14130f;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Params</th><td style="padding:9px 0;border:0;border-top:1.5px solid #14130f;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">27B</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Active</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">27B (dense)</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Size</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">18 GB</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Perplexity</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">6.63</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Refusals</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">—</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Context</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">256K</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">MTP head</th><td style="padding:9px 0;border:0;background:none;text-align:right;font-weight:700;color:#b5231c;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">bf16</td></tr></table></div>
 **TL;DR:** Qwopus3.6-27B-Coder, quantized to NVFP4 (W4A4) for vLLM on NVIDIA Blackwell. 18 GB, wikitext-2 PPL 6.63, 256K agentic coder.
@@ -164,4 +164,4 @@ the base model.
 - Base model: [Jackrong](https://huggingface.co/Jackrong)
 - Quantization tooling: [llm-compressor](https://github.com/vllm-project/llm-compressor) / [compressed-tensors](https://github.com/neuralmagic/compressed-tensors)
-<div style="font-family:ui-monospace,SFMono-Regular,Menlo,monospace;font-size:12px;color:#6f6b60;border-top:1.5px solid #14130f;padding-top:14px;margin-top:30px;">Part of <a href="https://huggingface.co/spaces/maci0/rogue-quants" style="color:#b5231c;font-weight:700;text-decoration:none;">Rogue Quants</a> — NVFP4 component datasheets &middot; <a href="https://huggingface.co/collections/maci0/nvfp4-quants-gb10-blackwell-6a446fc03174db196e436339" style="color:#b5231c;font-weight:700;text-decoration:none;">collection</a>. Fabricated on GB10 (Blackwell) with llm-compressor. Refusals shown per 100 harmful prompts; "—" = not separately measured (base-inherited).</div>

   - coder
 ---
+<div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;max-width:760px;border:1px solid #cec9ba;background:#f2efe6;color:#14130f;padding:24px 26px;border-radius:2px;margin-bottom:26px;"><div style="display:flex;justify-content:space-between;align-items:center;gap:10px 14px;flex-wrap:wrap;"><span style="display:inline-flex;align-items:center;gap:9px;"><svg width="18" height="18" viewBox="0 0 24 24" aria-hidden="true" style="flex-shrink:0;"><path d="M4 4 H16 L20 8 V20 H4 Z" fill="none" stroke="#6f6b60" stroke-width="1.25"/><path d="M16 4 V8 H20" fill="none" stroke="#6f6b60" stroke-width="1.25"/><circle cx="9" cy="13" r="1" fill="#6f6b60"/><circle cx="13" cy="15" r="1" fill="#6f6b60"/></svg><span style="font-family:ui-monospace,SFMono-Regular,Menlo,monospace;font-size:12px;font-weight:700;letter-spacing:0.02em;">RQ-27B-CODER</span></span></div><div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;font-size:26px;font-weight:800;letter-spacing:-0.02em;line-height:1.15;margin:18px 0 8px;color:#14130f;">Qwopus3.6-27B-Coder · NVFP4</div><div style="font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;font-size:14.5px;line-height:1.5;color:#4a4740;margin-bottom:18px;">27B agentic coder VL · tool-calling · thinking-mode reasoning (censored reference).</div><table style="width:100%;border-collapse:collapse;border:0;margin:2px 0 0;font-variant-numeric:tabular-nums;"><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-top:1.5px solid #14130f;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Params</th><td style="padding:9px 0;border:0;border-top:1.5px solid #14130f;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">27B</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Active</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">27B (dense)</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Size</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">18 GB</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Perplexity</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">6.63</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Refusals</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">n/a</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">Context</th><td style="padding:9px 0;border:0;border-bottom:1px solid #cec9ba;background:none;text-align:right;font-weight:700;color:#14130f;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">256K</td></tr><tr><th scope="row" style="text-align:left;font-weight:600;padding:9px 8px 9px 0;border:0;background:none;color:#4a4740;letter-spacing:0.01em;font-size:13px;font-family:ui-sans-serif,system-ui,-apple-system,sans-serif;">MTP head</th><td style="padding:9px 0;border:0;background:none;text-align:right;font-weight:700;color:#b5231c;font-size:14px;font-family:ui-monospace,SFMono-Regular,Menlo,monospace;white-space:nowrap;">bf16</td></tr></table></div>
 **TL;DR:** Qwopus3.6-27B-Coder, quantized to NVFP4 (W4A4) for vLLM on NVIDIA Blackwell. 18 GB, wikitext-2 PPL 6.63, 256K agentic coder.
 - Base model: [Jackrong](https://huggingface.co/Jackrong)
 - Quantization tooling: [llm-compressor](https://github.com/vllm-project/llm-compressor) / [compressed-tensors](https://github.com/neuralmagic/compressed-tensors)
+<div style="font-family:ui-monospace,SFMono-Regular,Menlo,monospace;font-size:12px;color:#6f6b60;border-top:1.5px solid #14130f;padding-top:14px;margin-top:30px;">Part of <a href="https://huggingface.co/spaces/maci0/rogue-quants" style="color:#b5231c;font-weight:700;text-decoration:none;">Rogue Quants</a> &middot; NVFP4 component datasheets &middot; <a href="https://huggingface.co/collections/maci0/nvfp4-quants-gb10-blackwell-6a446fc03174db196e436339" style="color:#b5231c;font-weight:700;text-decoration:none;">collection</a>. Fabricated on GB10 (Blackwell) with llm-compressor. Refusals shown per 100 harmful prompts; "n/a" = not separately measured (base-inherited).</div>