Instructions to use NguyenDinhHieu/Cube-Python-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NguyenDinhHieu/Cube-Python-1.0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NguyenDinhHieu/Cube-Python-1.0",
	filename="Cube-Python.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NguyenDinhHieu/Cube-Python-1.0 with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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

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-server -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

Hermes Agent new

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-server -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

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

Cube-Python-1.0 / README.md

NguyenDinhHieu

Update README.md

6bbdfd7 verified about 1 month ago

2.59 kB

license: mit
tags:
  - llm
  - gguf
  - llama
  - langchain
  - ctransformers
  - python
  - code
  - code-assistant
  - local-inference
  - multimodal
library_name: pytorch_lightning
pipeline_tag: text-generation
language:
  - en
  - vi

AI Python — Code Assistant (LangChain + CTransformers)

Demo chạy LLM dạng GGUF bằng ctransformers + langchain để trả lời theo prompt: “chỉ trả lời bằng code Python”.

Demo nhanh

Input: một yêu cầu/bài toán Python (text)
Output: chỉ code Python (không giải thích)

File chạy chính: app.py
Model mặc định: Cube-Python.gguf

Cài đặt

Tạo môi trường ảo (khuyến nghị) rồi cài dependencies:

pip install -U langchain langchain-community ctransformers

Chạy

Đảm bảo file model Cube-Python.gguf nằm cùng thư mục với app.py, rồi chạy:

python app.py

Cấu hình (trong `app.py`)

MODEL_FILE: tên file GGUF (mặc định Cube-Python.gguf)
MODEL_TYPE: loại model cho CTransformers (mặc định llama)
GPU_LAYERS:
- 0 = chạy CPU
- nếu máy có GPU VRAM đủ, tăng lên (ví dụ 10–20) để nhanh hơn
CONTEXT_LENGTH: độ dài ngữ cảnh (mặc định 4096)

Cấu trúc repo

app.py: prompt + chain (LangChain) + load model GGUF (CTransformers)
Cube-Python.gguf: file model GGUF

Lưu ý khi đẩy lên Hugging Face

File .gguf rất lớn: bạn nên dùng Git LFS khi push model lên Hub.
Nếu repo này là Model repo: giữ .gguf trong repo và thêm phần “Files and versions”.
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.

Flow đẩy lên Hugging Face (gợi ý)

Tạo repo trên Hugging Face (Model hoặc Space)
Clone repo về máy và copy các file (app.py, README.md, và/hoặc *.gguf)
Bật Git LFS cho GGUF rồi commit/push:

git lfs install
git lfs track "*.gguf"
git add .gitattributes
git add .
git commit -m "Add GGUF python code assistant demo"
git push

Ví dụ prompt

Bạn có thể thay biến question trong app.py bằng bài toán của bạn (tiếng Việt/tiếng Anh đều được).

Credits

LangChain
CTransformers

Nếu bạn thấy hay

Cho mình xin 1 follow trên Hugging Face và 1 tym (like) cho repo nhé. ❤️