Instructions to use xxue752/caiti_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xxue752/caiti_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xxue752/caiti_gguf",
	filename="Llama-3.2-3B-Instruct-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use xxue752/caiti_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xxue752/caiti_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf xxue752/caiti_gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf xxue752/caiti_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf xxue752/caiti_gguf:Q4_K_M

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 xxue752/caiti_gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf xxue752/caiti_gguf:Q4_K_M

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 xxue752/caiti_gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xxue752/caiti_gguf:Q4_K_M

Use Docker

docker model run hf.co/xxue752/caiti_gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use xxue752/caiti_gguf with Ollama:
```
ollama run hf.co/xxue752/caiti_gguf:Q4_K_M
```

Unsloth Studio new

How to use xxue752/caiti_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 xxue752/caiti_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 xxue752/caiti_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xxue752/caiti_gguf to start chatting

Pi new

How to use xxue752/caiti_gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf xxue752/caiti_gguf:Q4_K_M

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": "xxue752/caiti_gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use xxue752/caiti_gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf xxue752/caiti_gguf:Q4_K_M

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 xxue752/caiti_gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use xxue752/caiti_gguf with Docker Model Runner:
```
docker model run hf.co/xxue752/caiti_gguf:Q4_K_M
```

Lemonade

How to use xxue752/caiti_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xxue752/caiti_gguf:Q4_K_M

Run and chat with the model

lemonade run user.caiti_gguf-Q4_K_M

List all available models

lemonade list

CaiTI GGUF Bundle (Base + Task Adapters)

This repository contains the GGUF deployment artifacts for CaiTI using a single GGUF base model plus task-specific GGUF LoRA adapters.

Base model

Llama-3.2-3B-Instruct-Q4_K_M.gguf
Intended runtime: llama.cpp / llama-cpp-python

Task adapters

Use one adapter per task:

adapters/task1_response_analyzer.gguf -> Response Analyzer (37-dimension + score)
adapters/task2_general_response.gguf -> General Response (Yes/No/Maybe/Question/Stop)
adapters/task3_rv_reasoner.gguf -> RV reasoner (0/1)
adapters/task4_cbt_stage1.gguf -> CBT Stage 1
adapters/task4_cbt_stage2.gguf -> CBT Stage 2
adapters/task4_cbt_stage3.gguf -> CBT Stage 3

Runtime recommendation

Keep one base GGUF model loaded.
Switch adapters by task routing.
Use deterministic decoding for classification/reasoner tasks.

Example (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path='Llama-3.2-3B-Instruct-Q4_K_M.gguf',
    lora_path='adapters/task3_rv_reasoner.gguf',
    n_ctx=2048,
    n_gpu_layers=0,
)
out = llm('DECISION: 0/1 only\n...input...', max_tokens=8, temperature=0.0)
print(out['choices'][0]['text'])

Downloads last month: 135

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

View +6 variants

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