Image-Text-to-Text
Transformers
GGUF
text-generation-inference
unsloth
qwen3_5
reasoning
chain-of-thought
lora
sft
agent
tool-use
function-calling
coder
conversational
Instructions to use Jackrong/Qwopus3.5-9B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Jackrong/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-GGUF", dtype="auto") - llama-cpp-python
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Jackrong/Qwopus3.5-9B-Coder-GGUF", filename="Qwopus3.5-9B-coder-Exp-BF16.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jackrong/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
- SGLang
How to use Jackrong/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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" } } ] } ] }' - Ollama
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Ollama:
ollama run hf.co/Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
- Unsloth Studio new
How to use Jackrong/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-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/Qwopus3.5-9B-Coder-GGUF to start chatting
- Pi new
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Docker Model Runner:
docker model run hf.co/Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
- Lemonade
How to use Jackrong/Qwopus3.5-9B-Coder-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Jackrong/Qwopus3.5-9B-Coder-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwopus3.5-9B-Coder-GGUF-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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> [!IMPORTANT]
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### 🪐 SWE-bench Verified Performance (Repository-level Coding Capability)
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The following shows the comparative performance on **SWE-bench Verified**, which evaluates language models on resolving software engineering issues in real-world open-source repositories:
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<td colspan="3" style="padding: 8px 12px; font-weight: 600; color: #7c3aed; border-bottom: 1px solid rgba(124, 58, 237, 0.2); background: rgba(124, 58, 237, 0.05);">SWE-bench Verified Performance Metrics</td>
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<th style="padding: 7px 7px; padding-left: 20px; text-align: left; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Model</th>
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<th style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Test Set</th>
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<th style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Comprehensive Score (%)</th>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><span style="color: #666;">Claude 4.5 Opus</span></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">80.9</td>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/Qwen/Qwen3.5-27B" style="color: #666; text-decoration: none;">Qwen/Qwen3.5-27B</a></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">75.0</td>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/Qwen/Qwen3.6-35B-A3B" style="color: #666; text-decoration: none;">Qwen/Qwen3.6-35B-A3B</a></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">73.4</td>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><b><a href="https://huggingface.co/Jackrong/Qwopus3.5-9B-coder-GGUF" style="color: #7c3aed; text-decoration: none;">Qwopus3.5-9B-coder</a></b></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); color: #7c3aed; font-weight: bold;">53.33</td>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/google/gemma-4-31B-it" style="color: #666; text-decoration: none;">google/gemma-4-31B-it</a></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">52.0</td>
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<td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><span style="color: #666;">google/gemma-4-26B-A4B</span></td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
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<td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">45.0 - 48.0</td>
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> [!IMPORTANT]
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