Image-Text-to-Text
Transformers
Safetensors
qwen3_5
nvfp4
modelopt
nvidia
rtx-5090
blackwell
quantization
qwen3.8
sparkinfer
sglang
vllm
conversational
8-bit precision
Instructions to use gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090") 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090") model = AutoModelForMultimodalLM.from_pretrained("gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090", 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090", "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/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090
- SGLang
How to use gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 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 "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090" \ --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": "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090", "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 "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090" \ --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": "gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090", "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 gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090 with Docker Model Runner:
docker model run hf.co/gittensor-model-hub/Qwen3.8-27B-NVFP4-RTX5090
Qwen3.8-27B-NVFP4-RTX5090: FP8 KV-cache scales not shipped — vLLM runs uncalibrated (q_scale/prob_scale = 1.0)
#16 opened 7 days ago
by
diego-rivera
Can Any Model Be Quantized This Way?
#15 opened about 2 months ago
by
Duonglv
vLLM config: RTX 5090 + Qwen3.8-27B NVFP4 (~1580 tok/s concurrent / Ubuntu Server 24)
❤️ 5
4
#14 opened 2 months ago
by
leandro-lucena
Is the DSpark performance increase solely with SGlang?
#13 opened 2 months ago
by
anthonyya
Max-context, high-speed Qwen3.8-27B on a single 5090: SGLang + NVFP4 + DSpark drafter, with monitoring
❤️ 3
#12 opened 3 months ago
by
darksidewalker
Linux/SGLang/LMHead4/DSpark/TextOnly/236k context single 5090 GODSPEED recipe
#11 opened 3 months ago
by
cosmicnag
Share a simple deployment recipe: windows + docker + sglang + dspark (avg 120t/s 150K context)
❤️ 3
1
#10 opened 3 months ago
by
simonX1992
Qwen 3.8 + DSpark + vLLM
👀 2
2
#9 opened 3 months ago
by
gittyeric
Update chat template
4
#5 opened 3 months ago
by
reality-interface
MTP compatability
❤️ 1
4
#4 opened 3 months ago
by
KaleidRuby
Single RTX 5090 recipe: Docker stack (vLLM + MTP + 256k ctx + monitoring)
❤️ 6
2
#3 opened 3 months ago
by
DevBauerFlorian
Thankyou! what was the calibration dataset used?
1
#2 opened 3 months ago
by
SnowCode1
Thanks
#1 opened 3 months ago
by
jasionkajakub