Instructions to use kyaky/Qwen3.8-27B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kyaky/Qwen3.8-27B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="kyaky/Qwen3.8-27B-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("kyaky/Qwen3.8-27B-NVFP4") model = AutoModelForMultimodalLM.from_pretrained("kyaky/Qwen3.8-27B-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 kyaky/Qwen3.8-27B-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kyaky/Qwen3.8-27B-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": "kyaky/Qwen3.8-27B-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/kyaky/Qwen3.8-27B-NVFP4
- SGLang
How to use kyaky/Qwen3.8-27B-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 "kyaky/Qwen3.8-27B-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": "kyaky/Qwen3.8-27B-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 "kyaky/Qwen3.8-27B-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": "kyaky/Qwen3.8-27B-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 kyaky/Qwen3.8-27B-NVFP4 with Docker Model Runner:
docker model run hf.co/kyaky/Qwen3.8-27B-NVFP4
Qwen3.8-27B-NVFP4
A competitive, efficient NVFP4 build that matches or leads the alternative build across the measured benchmarks, stays measurably closer to the original BF16 model, and delivers faster single-stream generation on the tested hardware.
Highlights
- Matches or leads
unsloth/Qwen3.8-27B-NVFP4across CMMLU, C-Eval, and non-thinking MMLU-Pro, with MMLU-Pro essentially at parity. - Records 0.0265 KL divergence from BF16 versus 0.0391 for the competing build—about 32% lower and measurably closer to the original BF16 output distribution.
- Reaches 61.2 tokens/s single-stream versus 58.5 for the competing build and 27.4 for BF16 on the tested configuration—about 2.2× BF16.
- Delivers near-parity aggregate throughput at 32 concurrent requests: 1284 tokens/s versus 1292; higher concurrency tends to favor the competing build's FP4 kernel path.
- Downloads at 23.29 GB versus 23.4 GB for the competing build.
- NVFP4 4-bit weights for vLLM serving on NVIDIA Blackwell GPUs.
Results
| Metric | BF16 baseline | kyaky/Qwen3.8-27B-NVFP4 |
unsloth/Qwen3.8-27B-NVFP4 |
Result |
|---|---|---|---|---|
| CMMLU accuracy ↑ | 72.03 | 73.55 | 70.76 | +2.80 pp vs competitor |
| C-Eval accuracy ↑ | 74.07 | 75.93 | 73.63 | +2.30 pp vs competitor |
| MMLU-Pro accuracy ↑, non-thinking | 75.43 | 77.14 | 76.57 | Essentially at parity with competitor |
| KL divergence vs BF16 ↓ | 0 (reference) | 0.0265 | 0.0391 | About 32% lower than competitor |
| Download size ↓ | — | 23.29 GB | 23.4 GB | 0.11 GB smaller |
All comparisons use the same evaluation conditions. Small differences from the BF16 reference should be treated as evaluation variation, not as evidence that this build surpasses the base model's capabilities.
Performance (measured on 1 × NVIDIA RTX PRO 6000 only)
Measured on 1 × NVIDIA RTX PRO 6000 (Blackwell) using vLLM 0.25.1 with greedy decoding and an FP8 KV cache at 8K context. Workload: 256-token generations; single-stream is the mean of 3 runs, concurrent is 32 simultaneous requests. Values are tokens/second and apply only to this tested configuration; they should not be assumed to generalize to other hardware or serving setups.
| Metric | BF16 original | kyaky/Qwen3.8-27B-NVFP4 |
unsloth/Qwen3.8-27B-NVFP4 |
Result |
|---|---|---|---|---|
| Single-stream generation speed (tokens/s) ↑ | 27.4 | 61.2 | 58.5 | +4.6% vs competitor; about 2.2× BF16 |
| Aggregate throughput at 32 concurrent requests (tokens/s) ↑ | 648 | 1284 | 1292 | Near-parity at this concurrency; both roughly 2× BF16; higher concurrency tends to favor the competing build's FP4 kernel path |
Serve with vLLM
vllm serve kyaky/Qwen3.8-27B-NVFP4 --trust-remote-code
NVFP4 requires an NVIDIA Blackwell GPU, such as an RTX 50-series or RTX PRO 6000-class GPU, and is served through vLLM.
Intended use
Choose this release for a well-rounded 23.29 GB NVFP4 build with competitive benchmark retention, closer measured BF16 fidelity, and strong serving performance on the tested Blackwell configuration. It leads the competing build on CMMLU and C-Eval, remains essentially at parity on non-thinking MMLU-Pro, is faster in single-stream generation, and provides near-parity aggregate throughput at 32 concurrent requests.
kyaky/Qwen3.8-27B-NVFP4 is an independent NVFP4 release derived from Qwen/Qwen3.8-27B. Review the base model card for capabilities, acceptable use, and license terms.
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Qwen/Qwen3.8-27B