Instructions to use Agnuxo/CAJAL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnuxo/CAJAL-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Agnuxo/CAJAL-4B",
	filename="CAJAL-4B-f16.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 Agnuxo/CAJAL-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/CAJAL-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/CAJAL-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/CAJAL-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/CAJAL-4B: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 Agnuxo/CAJAL-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/CAJAL-4B: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 Agnuxo/CAJAL-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/CAJAL-4B:Q4_K_M

Use Docker

docker model run hf.co/Agnuxo/CAJAL-4B:Q4_K_M

LM Studio
Jan
Ollama
How to use Agnuxo/CAJAL-4B with Ollama:
```
ollama run hf.co/Agnuxo/CAJAL-4B:Q4_K_M
```

Unsloth Studio new

How to use Agnuxo/CAJAL-4B 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 Agnuxo/CAJAL-4B 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 Agnuxo/CAJAL-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Agnuxo/CAJAL-4B to start chatting

Pi new

How to use Agnuxo/CAJAL-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Agnuxo/CAJAL-4B: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": "CAJAL-4B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Agnuxo/CAJAL-4B with Docker Model Runner:
```
docker model run hf.co/Agnuxo/CAJAL-4B:Q4_K_M
```

Lemonade

How to use Agnuxo/CAJAL-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/CAJAL-4B:Q4_K_M

Run and chat with the model

lemonade run user.CAJAL-4B-Q4_K_M

List all available models

lemonade list

CAJAL-4B: Autonomous BFT Research Paper Generator

CAJAL-4B is a 4B-parameter model specialized for generating Byzantine Fault Tolerant (BFT) consensus research papers.

Models

Quantization	Size	Use Case
CAJAL-4B-q4_k_m.gguf	2.7 GB	Low VRAM (<4GB)
CAJAL-4B-q8_0.gguf	4.5 GB	Balanced
CAJAL-4B-f16.gguf	8.4 GB	Highest quality

Use with llama.cpp

llama-cli -hf Agnuxo/CAJAL-4B:Q4_K_M -n 512 \
  --temp 0.42 -p "Write BFT abstract..."

Use with Python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Agnuxo/CAJAL-4B",
    filename="CAJAL-4B-q4_k_m.gguf"
)

Results

Papers published: 36+ on p2pclaw.com
Best score: 7.0/10
Target: ≥8/10

Repository

GitHub: https://github.com/Agnuxo1/CAJAL
Paper Harness: harness.py (included)

License

Apache 2.0

Generated: 2025-05-07

Downloads last month: 57

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

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