Instructions to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF") 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 AutoModel model = AutoModel.from_pretrained("Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF", "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/Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF
- SGLang
How to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF 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 "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF" \ --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": "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF", "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 "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF" \ --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": "Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF", "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" } } ] } ] }' - Unsloth Studio
How to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF", max_seq_length=2048, ) - Docker Model Runner
How to use Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF with Docker Model Runner:
docker model run hf.co/Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF
Thinking has issues
I have had to type in again "continue" to the CLI using Q5KM model at Q8KVCache with thinking enabled, it stops thinking and nothing happens until I type something again - have only experienced this issue with this model.
With Q6_K at Q_8 KV cache it just massively overthought every single thing over and over again to a degree I've never seen the base Qwen3.6 27B do. Definitely needs some figuring out to get it to stop rethinking a simple thing and burning tokens.
i wonder how that Boat Survival game was made and what was the promt?
EDIT: Updated llama.cpp and it's better now. Hasn't repeated yet!
OLD: It is repeating for me a lot. And the code I get is very basic...
Thinking should be off based on the model card.....
Thinking should be off based on the model card.....
yeah, I think the same, it's a coder model, not an orchestrator
Hi everyone, thanks for the feedback and for sharing your findings!
I’ve just pushed an update to a new repo (Jackrong/Qwopus3.6-27B-Coder-Compat-MTP-GGUF) with a fixed chat template to address some agent-related loop issues, so feel free to give that a try!