Text Generation
GGUF
English
Vietnamese
pytorch_lightning
llm
llama
langchain
ctransformers
python
code
code-assistant
local-inference
multimodal
imatrix
conversational
Instructions to use NguyenDinhHieu/Cube-Python-1.0 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 NguyenDinhHieu/Cube-Python-1.0 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 NguyenDinhHieu/Cube-Python-1.0 # Run inference directly in the terminal: llama cli -hf NguyenDinhHieu/Cube-Python-1.0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NguyenDinhHieu/Cube-Python-1.0 # Run inference directly in the terminal: llama cli -hf NguyenDinhHieu/Cube-Python-1.0
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 NguyenDinhHieu/Cube-Python-1.0 # Run inference directly in the terminal: ./llama-cli -hf NguyenDinhHieu/Cube-Python-1.0
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 NguyenDinhHieu/Cube-Python-1.0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf NguyenDinhHieu/Cube-Python-1.0
Use Docker
docker model run hf.co/NguyenDinhHieu/Cube-Python-1.0
- LM Studio
- Jan
- vLLM
How to use NguyenDinhHieu/Cube-Python-1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NguyenDinhHieu/Cube-Python-1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NguyenDinhHieu/Cube-Python-1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NguyenDinhHieu/Cube-Python-1.0
- Ollama
How to use NguyenDinhHieu/Cube-Python-1.0 with Ollama:
ollama run hf.co/NguyenDinhHieu/Cube-Python-1.0
- Unsloth Studio
How to use NguyenDinhHieu/Cube-Python-1.0 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 NguyenDinhHieu/Cube-Python-1.0 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 NguyenDinhHieu/Cube-Python-1.0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NguyenDinhHieu/Cube-Python-1.0 to start chatting
- Pi
How to use NguyenDinhHieu/Cube-Python-1.0 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NguyenDinhHieu/Cube-Python-1.0
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": "NguyenDinhHieu/Cube-Python-1.0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use NguyenDinhHieu/Cube-Python-1.0 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NguyenDinhHieu/Cube-Python-1.0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "NguyenDinhHieu/Cube-Python-1.0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use NguyenDinhHieu/Cube-Python-1.0 with Docker Model Runner:
docker model run hf.co/NguyenDinhHieu/Cube-Python-1.0
- Lemonade
How to use NguyenDinhHieu/Cube-Python-1.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NguyenDinhHieu/Cube-Python-1.0
Run and chat with the model
lemonade run user.Cube-Python-1.0-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use NguyenDinhHieu/Cube-Python-1.0 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NguyenDinhHieu/Cube-Python-1.0
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 NguyenDinhHieu/Cube-Python-1.0
Run Hermes
hermes
- Atomic Chat
Update README.md
Browse files
README.md
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license: mit
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---
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license: mit
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tags:
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- llm
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- gguf
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- llama
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- langchain
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- ctransformers
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- python
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- code
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- code-assistant
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- local-inference
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- multimodal
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library_name: pytorch_lightning
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pipeline_tag: text-generation
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language:
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- en
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- vi
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---
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# AI Python — Code Assistant (LangChain + CTransformers)
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Demo chạy **LLM dạng GGUF** bằng `ctransformers` + `langchain` để trả lời theo prompt: **“chỉ trả lời bằng code Python”**.
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## Demo nhanh
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- **Input**: một yêu cầu/bài toán Python (text)
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- **Output**: **chỉ code Python** (không giải thích)
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File chạy chính: `app.py`
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Model mặc định: `Cube-Python.gguf`
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## Cài đặt
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Tạo môi trường ảo (khuyến nghị) rồi cài dependencies:
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```bash
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pip install -U langchain langchain-community ctransformers
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```
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## Chạy
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Đảm bảo file model `Cube-Python.gguf` nằm cùng thư mục với `app.py`, rồi chạy:
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```bash
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python app.py
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```
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## Cấu hình (trong `app.py`)
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- **`MODEL_FILE`**: tên file GGUF (mặc định `Cube-Python.gguf`)
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- **`MODEL_TYPE`**: loại model cho CTransformers (mặc định `llama`)
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- **`GPU_LAYERS`**:
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- `0` = chạy CPU
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- nếu máy có GPU VRAM đủ, tăng lên (ví dụ 10–20) để nhanh hơn
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- **`CONTEXT_LENGTH`**: độ dài ngữ cảnh (mặc định `4096`)
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## Cấu trúc repo
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- `app.py`: prompt + chain (LangChain) + load model GGUF (CTransformers)
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- `Cube-Python.gguf`: file model GGUF
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## Lưu ý khi đẩy lên Hugging Face
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- **File `.gguf` rất lớn**: bạn nên dùng **Git LFS** khi push model lên Hub.
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- Nếu repo này là **Model repo**: giữ `.gguf` trong repo và thêm phần “Files and versions”.
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- Nếu repo này là **Space**: cân nhắc **không** commit file GGUF trực tiếp (thường vượt giới hạn), thay vào đó tải từ Model repo hoặc từ Release/Storage phù hợp.
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### Flow đẩy lên Hugging Face (gợi ý)
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1) **Tạo repo trên Hugging Face** (Model hoặc Space)
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2) **Clone repo về máy** và copy các file (`app.py`, `README.md`, và/hoặc `*.gguf`)
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3) **Bật Git LFS cho GGUF** rồi commit/push:
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```bash
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git lfs install
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git lfs track "*.gguf"
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git add .gitattributes
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git add .
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git commit -m "Add GGUF python code assistant demo"
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git push
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```
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## Ví dụ prompt
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Bạn có thể thay biến `question` trong `app.py` bằng bài toán của bạn (tiếng Việt/tiếng Anh đều được).
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## Credits
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- LangChain
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- CTransformers
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## Nếu bạn thấy hay
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Cho mình xin **1 follow** trên Hugging Face và **1 tym** (like) cho repo nhé. ❤️
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