Instructions to use build-small-hackathon/phase-3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/phase-3-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/phase-3-gguf",
	filename="model-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use build-small-hackathon/phase-3-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 build-small-hackathon/phase-3-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/phase-3-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf build-small-hackathon/phase-3-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/phase-3-gguf:Q8_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 build-small-hackathon/phase-3-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/phase-3-gguf:Q8_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 build-small-hackathon/phase-3-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/phase-3-gguf:Q8_0

Use Docker

docker model run hf.co/build-small-hackathon/phase-3-gguf:Q8_0

LM Studio
Jan
Ollama
How to use build-small-hackathon/phase-3-gguf with Ollama:
```
ollama run hf.co/build-small-hackathon/phase-3-gguf:Q8_0
```

Unsloth Studio

How to use build-small-hackathon/phase-3-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 build-small-hackathon/phase-3-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 build-small-hackathon/phase-3-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/phase-3-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use build-small-hackathon/phase-3-gguf with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/phase-3-gguf:Q8_0
```

Lemonade

How to use build-small-hackathon/phase-3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/phase-3-gguf:Q8_0

Run and chat with the model

lemonade run user.phase-3-gguf-Q8_0

List all available models

lemonade list

phase-3-gguf / README.md

owenisas

Update org model card

2abd170 verified about 1 month ago

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metadata

license: mit
library_name: llama-cpp
tags:
  - gguf
  - q8_0
  - llama-cpp
  - local-llm
  - build-small-hackathon

Phase-3 GGUF

This repository hosts the Phase-3 GGUF artifact used by the public Gradio Space build-small-hackathon/First-Principle-AI.

File

model-Q8_0.gguf
Local source before upload: /Users/user/.lmstudio/models/owenisas/Phase-3-GGUF/model-Q8_0.gguf
Quantization label: Q8_0
Approximate local size: 31 GB

Intended Use

The paired Space loads this artifact with llama-cpp-python and exposes a dense model-lab chatbot with sampling controls and runtime diagnostics.

Runtime Caveat

This is a large Q8 GGUF. It needs substantially more memory than a typical free CPU Space can provide. The paired Space is configured to target ZeroGPU, but ZeroGPU compatibility with llama.cpp/GGUF should be verified at runtime.