Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
qwen3.6
qwopus
gptq
gptq-pro
marlin
vllm
int4
quantized
mmlu-pro
25-may-update
conversational
4-bit precision
Instructions to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1") 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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1") model = AutoModelForImageTextToText.from_pretrained("XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1") 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 XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1
- SGLang
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 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 "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \ --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": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "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 "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1" \ --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": "XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1 with Docker Model Runner:
docker model run hf.co/XReyRobert/Qwopus3.6-27B-v2-GPTQ-Pro-v1

- Xet hash:
- 38812749444999b557893440dab76992d89d56a6ca20c592e88d7d575d9ea869
- Size of remote file:
- 2.17 MB
- SHA256:
- f15ffde4999c7989c2b49a51f0c14b962e46e5c87babf6926335f9d0f4503f3b
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