Instructions to use build-small-hackathon/codeflow-qwen-3-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use build-small-hackathon/codeflow-qwen-3-finetuning with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="build-small-hackathon/codeflow-qwen-3-finetuning", filename="qwen3-coder-codeflow-Q3_K_L.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use build-small-hackathon/codeflow-qwen-3-finetuning with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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 build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: ./llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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 build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: ./build/bin/llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Use Docker
docker model run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- LM Studio
- Jan
- Ollama
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Ollama:
ollama run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- Unsloth Studio
How to use build-small-hackathon/codeflow-qwen-3-finetuning 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 build-small-hackathon/codeflow-qwen-3-finetuning 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 build-small-hackathon/codeflow-qwen-3-finetuning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for build-small-hackathon/codeflow-qwen-3-finetuning to start chatting
- Pi
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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": "build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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 build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Docker Model Runner:
docker model run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- Lemonade
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Run and chat with the model
lemonade run user.codeflow-qwen-3-finetuning-Q3_K_L
List all available models
lemonade list
| # Dataset preview | |
| Random sample of 6 examples (system prompt omitted for brevity). | |
| ## Example 1 — `switch_match` (c), 6 nodes | |
| **User (input):** | |
| ``` | |
| 1| const char* trim_value(int kind) { | |
| 2| switch (kind) { | |
| 3| case 0: | |
| 4| return "first"; | |
| 5| case 1: | |
| 6| return "second"; | |
| 7| case 2: | |
| 8| return "third"; | |
| 9| default: | |
| 10| return "other"; | |
| 11| } | |
| 12| } | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Control structures: 1 decision point, 4 terminal branches. | |
| 2. Execution nodes in order: A Start: trim_value, B Which case applies?, C Return first label, D Return second label, E Return third label, F Return default label. | |
| 3. Source lines: A maps to line 1, B maps to line 2, C maps to line 4, D maps to line 6, E maps to line 8, F maps to line 10. | |
| </thinking> | |
| graph TD | |
| A[Start: trim_value] | |
| B{Which case applies?} | |
| C[Return first label] | |
| D[Return second label] | |
| E[Return third label] | |
| F[Return default label] | |
| A --> B | |
| B -- First case --> C | |
| B -- Second case --> D | |
| B -- Third case --> E | |
| B -- Default --> F | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 4 | |
| D: 6 | |
| E: 8 | |
| F: 10 | |
| </linemap> | |
| ``` | |
| ## Example 2 — `guard_return` (c), 4 nodes | |
| **User (input):** | |
| ``` | |
| 1| int aggregate(int found) { | |
| 2| if (found) { | |
| 3| return 0; | |
| 4| } | |
| 5| return 1; | |
| 6| } | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Structural parse: 1 decision point, 2 terminal branches. | |
| 2. Execution nodes in order: A Start: aggregate, B Flag is set?, C Return false, D Return true. | |
| 3. Source lines: A maps to line 1, B maps to line 2, C maps to line 3, D maps to line 5. | |
| </thinking> | |
| graph TD | |
| A[Start: aggregate] | |
| B{Flag is set?} | |
| C[Return false] | |
| D[Return true] | |
| A --> B | |
| B -- True --> C | |
| B -- False --> D | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 3 | |
| D: 5 | |
| </linemap> | |
| ``` | |
| ## Example 3 — `dict_lookup` (javascript), 4 nodes | |
| **User (input):** | |
| ``` | |
| 1| function searchList(registry, field) { | |
| 2| if (field in registry) { | |
| 3| return registry[field]; | |
| 4| } | |
| 5| return null; | |
| 6| } | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Structural parse: 1 decision point, 2 terminal branches. | |
| 2. Nodes mapped chronologically: A Start: searchList, B Key exists in map?, C Return the mapped value, D Return nothing. | |
| 3. Source lines: A maps to line 1, B maps to line 2, C maps to line 3, D maps to line 5. | |
| </thinking> | |
| graph TD | |
| A[Start: searchList] | |
| B{Key exists in map?} | |
| C[Return the mapped value] | |
| D[Return nothing] | |
| A --> B | |
| B -- Found --> C | |
| B -- Missing --> D | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 3 | |
| D: 5 | |
| </linemap> | |
| ``` | |
| ## Example 4 — `try_except` (javascript), 4 nodes | |
| **User (input):** | |
| ``` | |
| 1| function formatName(line) { | |
| 2| try { | |
| 3| return parseInt(line); | |
| 4| } catch (err) { | |
| 5| return 0; | |
| 6| } | |
| 7| } | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Control structures: 1 decision point, 2 terminal branches. | |
| 2. Node sequence: A Start: formatName, B Conversion succeeds?, C Return the value, D Return zero. | |
| 3. Source lines: A maps to line 1, B maps to line 2, C maps to line 3, D maps to line 5. | |
| </thinking> | |
| graph TD | |
| A[Start: formatName] | |
| B{Conversion succeeds?} | |
| C[Return the value] | |
| D[Return zero] | |
| A --> B | |
| B -- Success --> C | |
| B -- Failure --> D | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 3 | |
| D: 5 | |
| </linemap> | |
| ``` | |
| ## Example 5 — `for_search` (javascript), 5 nodes | |
| **User (input):** | |
| ``` | |
| 1| function lookup(results, wanted) { | |
| 2| for (let i = 0; i < results.length; i++) { | |
| 3| if (results[i] === wanted) { | |
| 4| return i; | |
| 5| } | |
| 6| } | |
| 7| return -1; | |
| 8| } | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Structural parse: 1 decision point, 1 loop, 2 terminal branches. | |
| 2. Execution nodes in order: A Start: lookup, B Iterate over collection, C Item matches target?, D Return the index, E Return not found. | |
| 3. Source lines: A is line 1, B is line 2, C is line 3, D is line 4, E is line 7. | |
| </thinking> | |
| graph TD | |
| A[Start: lookup] | |
| B[Iterate over collection] | |
| C{Item matches target?} | |
| D[Return the index] | |
| E[Return not found] | |
| A --> B | |
| B -- For each --> C | |
| B -- No more items --> E | |
| C -- Match --> D | |
| C -- No match --> B | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 3 | |
| D: 4 | |
| E: 7 | |
| </linemap> | |
| ``` | |
| ## Example 6 — `dict_lookup` (python), 4 nodes | |
| **User (input):** | |
| ``` | |
| 1| def lookup(mapping, user_id): | |
| 2| if user_id in mapping: | |
| 3| return mapping[user_id] | |
| 4| return None | |
| ``` | |
| **Assistant (target):** | |
| ``` | |
| <thinking> | |
| 1. Structural parse: 1 decision point, 2 terminal branches. | |
| 2. Node sequence: A Start: lookup, B Key exists in map?, C Return the mapped value, D Return nothing. | |
| 3. Source lines: A maps to line 1, B maps to line 2, C maps to line 3, D maps to line 4. | |
| </thinking> | |
| graph TD | |
| A[Start: lookup] | |
| B{Key exists in map?} | |
| C[Return the mapped value] | |
| D[Return nothing] | |
| A --> B | |
| B -- Found --> C | |
| B -- Missing --> D | |
| <linemap> | |
| A: 1 | |
| B: 2 | |
| C: 3 | |
| D: 4 | |
| </linemap> | |
| ``` | |