Instructions to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf 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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf 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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16 # Run inference directly in the terminal: llama cli -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16 # Run inference directly in the terminal: llama cli -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
Use Docker
docker model run hf.co/JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
- LM Studio
- Jan
- Ollama
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with Ollama:
ollama run hf.co/JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
- Unsloth Studio
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf 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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf 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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf to start chatting
- Pi
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
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": "JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with Docker Model Runner:
docker model run hf.co/JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
- Lemonade
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
Run and chat with the model
lemonade run user.Llama-3.2-1B-unal-instruct-ft-gguf-F16
List all available models
lemonade list
- Hermes Agent
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
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 JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16
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 "JulianVelandia/Llama-3.2-1B-unal-instruct-ft-gguf:F16" \ --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"
Llama 3.2-1B UNAL Instruct FT
Descripción
Modelo basado en Meta-Llama-3.2-1B, ajustado con LoRA para tareas de generación de texto en español. Fue entrenado utilizando el dataset Grade Works UNAL Dataset Instruct, el cual contiene preguntas y respuestas derivadas de trabajos de grado de la Universidad Nacional de Colombia.
El entrenamiento se realizó en Google Colab Free con GPU, de manera diferida, y tomó aproximadamente 7 horas.
Notebook
https://github.com/julianVelandia/FinetuningLLMGradeWorksUNALDatasetInstruct
Características
- Modelo base: Meta-Llama-3.2-1B.
- Técnica de ajuste: LoRA (Low-Rank Adaptation).
- Formato de entrenamiento: Instrucción basada en pares pregunta-respuesta.
- Idiomas soportados: Español.
- Tamaño: 1B parámetros.
Dataset utilizado
El modelo fue entrenado con el dataset Grade Works UNAL Dataset Instruct, que contiene:
- 16,700 registros de pares pregunta-respuesta.
- Fuente: Trabajos de grado de la Universidad Nacional de Colombia.
- Formato:
prompt(pregunta),completion(respuesta),fragment(texto base).
Licencia
Apache 2.0
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