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.
- Libraries
- llama-cpp-python
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
- 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 new
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 new
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
Upload app.py
Browse files
app.py
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_community.llms import CTransformers
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import os
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# Cài thư việt trước
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# ! pip install langchain langchain-community ctransformers
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MODEL_FILE = "Cube-Python.gguf"
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MODEL_TYPE = "llama"
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GPU_LAYERS = 0 # Nếu máy có GPU VRAM nhiều hơn có thể chỉnh lên 10-20 để trả lời nhanh hơn
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CONTEXT_LENGTH = 4096
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def load_llm_with_ctransformers():
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model_path = os.path.join(os.getcwd(), MODEL_FILE)
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"File mô hình {MODEL_FILE} không tồn tại.")
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llm = CTransformers(
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model=model_path,
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model_type=MODEL_TYPE,
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config={
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'max_new_tokens': 1024,
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'temperature': 0.1,
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'gpu_layers': GPU_LAYERS,
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'context_length': CONTEXT_LENGTH,
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}
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)
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return llm
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template = """[INST] Bạn là một trợ lý AI chuyên nghiệp về lập trình Python.
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Hãy viết code Python chất lượng cao để giải quyết yêu cầu sau.
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Ưu tiên:
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- code rõ ràng
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- có thể chạy được
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- đặt tên biến dễ hiểu
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Chỉ trả lời bằng code.
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Yêu cầu: {question} [/INST]"""
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prompt = PromptTemplate(
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input_variables=["question"],
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template=template
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)
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llm = load_llm_with_ctransformers()
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parser = StrOutputParser()
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chain = prompt | llm | parser
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question = '''
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Write a Python program that takes a sentence as input and removes all punctuation marks
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from it.
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Input:
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A sentence: "Hello! This is a NLP practical exam, isn't it?"
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Desired Output:
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Hello This is a NLP practical exam isnt it'''
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result = chain.invoke({"question": question})
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print(result)
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