cajal-p2pclaw 1.0.0

CAJAL-4B — Native integration for the P2PCLAW scientific intelligence model. Easy inference, chat, and server.

CAJAL-4B-P2PCLAW — Native Python Integration

🧠 One-line install: pip install cajal

CAJAL-4B is a specialized scientific intelligence model fine-tuned for decentralized research networks, peer-to-peer architectures, cryptographic protocols, and formal verification. This package provides native Python integration, a FastAPI server, CLI tool, and configs for 10+ platforms.

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Quick Start

1. Install

pip install cajal

2. Chat in Python

from cajal import CAJALChat

chat = CAJALChat()
response = chat.send("Explain Byzantine consensus in P2P networks.")
print(response)

3. Run Server (OpenAI-compatible)

cajal-server --port 8000
# Endpoint: POST /v1/chat/completions

4. CLI Chat

cajal "Explain zero-knowledge proofs"
# Or interactive mode:
cajal -i

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Ollama Integration (Recommended)

# Pull the model
ollama pull Agnuxo/CAJAL-4B-P2PCLAW

# Create custom model with system prompt
ollama create cajal -f Modelfile

# Run
ollama run cajal

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Platform Integrations

Platform	Config	Status
Ollama	integrations/ollama/Modelfile	✅ Ready
VS Code	integrations/vscode/cajal.json	✅ Ready
Cursor	integrations/cursor/cajal.json	✅ Ready
Continue.dev	integrations/continue_dev/config.yaml	✅ Ready
Open WebUI	integrations/openwebui/README.md	✅ Ready
Jan	integrations/jan/model.json	✅ Ready
LM Studio	integrations/lmstudio/README.md	✅ Ready
Pinokio	integrations/pinokio/install.json	✅ Ready

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Model Info

• Base: Qwen3.5-4B (Apache 2.0)
• Fine-tuned: 4.21B parameters
• Context: 262K tokens
• Format: Safetensors BF16
• License: MIT
• HF: Agnuxo/CAJAL-4B-P2PCLAW

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Requirements

• Python 3.9+
• PyTorch 2.2+
• Transformers 4.40+
• GPU recommended (6.5GB VRAM minimum)

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License

MIT — Francisco Angulo de Lafuente (Agnuxo1)

Copyright 2026 P2PCLAW Laboratory