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 = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Agnuxo/CAJAL-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agnuxo/CAJAL-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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": "Agnuxo/CAJAL-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Agnuxo/CAJAL-4B with Hermes Agent:

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 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 Agnuxo/CAJAL-4B:Q4_K_M

Run Hermes

hermes

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

Agnuxo commited on 4 days ago

Commit

7ade7cb

verified ·

1 Parent(s): 574b462

Add Model Card

Browse files

Files changed (1) hide show

README.md +62 -0

README.md ADDED Viewed

	@@ -0,0 +1,62 @@

+---
+license: apache-2.0
+library_name: llama.cpp
+tags:
+- bft
+- consensus
+- distributed-systems
+- quantized
+- 4b
+- paper-generation
+- blockchain
+- byzantine-fault-tolerance
+---
+# 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
+```bash
+llama-cli -hf Agnuxo/CAJAL-4B:Q4_K_M -n 512 \
+  --temp 0.42 -p "Write BFT abstract..."
+```
+## Use with Python
+```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*