Instructions to use Macmill/Fyve-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Macmill/Fyve-AI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Macmill/Fyve-AI:Q4_K_M # Run inference directly in the terminal: llama cli -hf Macmill/Fyve-AI:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Macmill/Fyve-AI:Q4_K_M # Run inference directly in the terminal: llama cli -hf Macmill/Fyve-AI: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 Macmill/Fyve-AI:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Macmill/Fyve-AI: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 Macmill/Fyve-AI:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Macmill/Fyve-AI:Q4_K_M
Use Docker
docker model run hf.co/Macmill/Fyve-AI:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Macmill/Fyve-AI with Ollama:
ollama run hf.co/Macmill/Fyve-AI:Q4_K_M
- Unsloth Studio
How to use Macmill/Fyve-AI 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 Macmill/Fyve-AI 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 Macmill/Fyve-AI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Macmill/Fyve-AI to start chatting
- Docker Model Runner
How to use Macmill/Fyve-AI with Docker Model Runner:
docker model run hf.co/Macmill/Fyve-AI:Q4_K_M
- Lemonade
How to use Macmill/Fyve-AI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Macmill/Fyve-AI:Q4_K_M
Run and chat with the model
lemonade run user.Fyve-AI-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| language: | |
| - en | |
| license: other | |
| license_name: qianwen | |
| license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE | |
| base_model: | |
| - Qwen/Qwen3-4B | |
| tags: | |
| - fine-tuned | |
| - education | |
| - python | |
| - socratic | |
| - qlora | |
| - unsloth | |
| # Fyve-AI | |
| **Fyve-AI** is a fine-tuned version of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) trained for one specific task: reading a student's broken Python code and responding with a Socratic 3-sentence hint β not the answer. | |
| It is the AI model powering [PyFyve](https://github.com/Macmill-340/PyFyve), a fully offline Python tutoring application. | |
| --- | |
| ## What It Does | |
| Given a task description, the student's buggy code, and the Python error it produced, the model outputs a JSON object with two fields: | |
| - `reasoning` β internal diagnosis of what went wrong and why | |
| - `hint` β exactly 3 sentences following a fixed structure: | |
| 1. **Diagnosis** β names the specific variable, expression, or construct that caused the error | |
| 2. **Rule** β states the Python rule that was violated | |
| 3. **Directive** β starts with *"Think about..."* or *"Consider..."* and guides without giving the fix | |
| The model never gives corrected code. It never gives more than 3 sentences. It does not know how to do anything outside this task. | |
| --- | |
| ## Input Format | |
| ``` | |
| Task: | |
| <what the student was asked to do> | |
| Code: | |
| <the student's broken code> | |
| Error: | |
| <the Python error message> | |
| ``` | |
| --- | |
| ## Output Format | |
| ```json | |
| { | |
| "reasoning": "...", | |
| "hint": "Sentence 1.\nSentence 2.\nSentence 3." | |
| } | |
| ``` | |
| --- | |
| ## Example | |
| **Input:** | |
| ``` | |
| Task: | |
| Create a variable score = 95 and print its value. | |
| Code: | |
| score = 95 | |
| print(Score) | |
| Error: | |
| NameError: name 'Score' is not defined at line 2 | |
| ``` | |
| **Output:** | |
| ```json | |
| { | |
| "reasoning": "score is defined lowercase but Score (capital S) is used in print. I name both spellings, explain case sensitivity, and direct toward comparing the two usages.", | |
| "hint": "You defined a variable called score on line 1 but referenced Score on line 2.\nIn Python, variable names are case-sensitive, so score and Score are treated as two completely different identifiers.\nConsider whether the capitalisation of the variable name is consistent between where it was defined and where it is used." | |
| } | |
| ``` | |
| --- | |
| ## Training Details | |
| | Detail | Value | | |
| |--------|-------| | |
| | Base model | Qwen3-4B | | |
| | Method | QLoRA via [Unsloth](https://github.com/unslothai/unsloth) | | |
| | Hardware | Google Colab T4 (free tier) | | |
| | Dataset | 555 curated (task, code, error, hint) pairs | | |
| | Dataset source | Synthetic β generated using Qwen3-30B-A3B as teacher model | | |
| | Error types covered | SyntaxError, NameError, TypeError, IndexError, KeyError, ValueError, AttributeError, UnboundLocalError, RecursionError, ZeroDivisionError, and more | | |
| The training data was generated by a 30B teacher model, manually reviewed for quality, and filtered through a validation pipeline that checks hint structure, sentence count, and semantic rules (e.g. AttributeError on strings must guide toward `+` or `+=`, not list conversion). | |
| --- | |
| ## Intended Use | |
| This model is designed exclusively for use inside the PyFyve app. It is not a general-purpose assistant and will produce poor results for tasks outside its training distribution. | |
| **It is not designed to:** | |
| - Answer general Python questions | |
| - Explain concepts freely | |
| - Write or complete code | |
| - Serve as a chatbot | |
| --- | |
| ## Limitations | |
| - Trained on 555 examples β covers common beginner and intermediate Python errors well, but unusual or advanced errors may produce weaker hints | |
| - No coverage of logic errors (code that runs but produces wrong output) | |
| - Some uncommon syntax patterns (e.g. trailing comma creating a tuple) are outside the training distribution | |
| - The 3-sentence format is enforced by the prompt at inference time β removing the few-shot examples from the prompt degrades output quality significantly | |
| --- | |
| ## Usage with Ollama | |
| This model is distributed as a GGUF file for use with [Ollama](https://ollama.com). The `Modelfile` in this repository contains the Ollama model definition. | |
| ```bash | |
| ollama create fyve-ai -f Modelfile | |
| ``` | |
| Or use the PyFyve app, which handles setup automatically. | |
| --- | |
| ## License | |
| The fine-tuned weights are released under the same license as the base model: the [Apache License](https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE). | |
| Please read it before redistributing β it permits research and personal use but has restrictions on commercial use above certain usage thresholds. | |
| --- | |
| ## Citation | |
| If you use this model in research or build on it, please link back to the [PyFyve repository](https://github.com/Macmill-340/PyFyve). | |