CAJAL-4B-P2PCLAW

Autonomous Scientific Research Agent — Fine-tuned from Qwen3.5-4B for the P2PCLAW ecosystem

Overview

CAJAL-4B-P2PCLAW is a fine-tuned language model specialized in autonomous scientific research and paper writing within the P2PCLAW (Peer-to-Peer Crypto Law) ecosystem. Built on top of Qwen3.5-4B using QLoRA (4-bit NF4 quantization with LoRA adapters), it follows a rigorous 14-step paper-writing procedure that includes arXiv review, P2PCLAW rule compliance, claim verification, and Lean4 proof checking.

Key Features

• 14-Step Paper Writing Procedure: Intent analysis → arXiv review → draft → compliance check → API enrichment → plan → verify claims → real data → test code → write paper → Lean4 verify → submit → score
• P2PCLAW Integration: Native understanding of P2PCLAW rules, constitution, and submission workflows
• Game-Theoretic Analysis: Specialized in game theory, consensus mechanisms, and distributed systems
• Multi-format Output: Generates LaTeX papers, Python code, Lean4 proofs, and structured analysis

Quick Start

Using with 🤗 Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Agnuxo/CAJAL-4B-P2PCLAW",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Agnuxo/CAJAL-4B-P2PCLAW")

messages = [
    {"role": "system", "content": "You are CAJAL-4B, an autonomous research agent..."},
    {"role": "user", "content": "Write a paper about Nash equilibria in blockchain governance"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Using with 🦙 Ollama

# Install Ollama from https://ollama.com
ollama run agnuxo/cajal-4b-p2pclaw

# Or create from Modelfile:
curl -O https://huggingface.co/Agnuxo/CAJAL-4B-P2PCLAW/resolve/main/Modelfile
ollama create cajal-4b -f Modelfile
ollama run cajal-4b

Using with 🖥️ LM Studio

1. Download the GGUF quantized version from the Files tab
2. Open LM Studio → File → Import Model → Select the .gguf file
3. Start chatting!

Using with llama.cpp

# Download GGUF file
wget https://huggingface.co/Agnuxo/CAJAL-4B-P2PCLAW/resolve/main/cajal-4b-p2pclaw-Q4_K_M.gguf

# Run inference
./llama-cli -m cajal-4b-p2pclaw-Q4_K_M.gguf -p "Write a paper about..." -ngl 32

Using with vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="Agnuxo/CAJAL-4B-P2PCLAW", trust_remote_code=True)
params = SamplingParams(max_tokens=4096, temperature=0.7)
output = llm.generate("Write a scientific paper about decentralized governance", params)
print(output[0].outputs[0].text)

Using with Python (pip)

pip install cajal
cajal chat  # Interactive CLI
cajal serve # OpenAI-compatible API server on port 8765

Using with API (OpenAI-compatible)

import openai

client = openai.OpenAI(
    base_url="http://localhost:8765/v1",
    api_key="cajal"
)
response = client.chat.completions.create(
    model="cajal-4b",
    messages=[{"role": "user", "content": "Analyze Nash equilibria in P2P networks"}]
)
print(response.choices[0].message.content)

Model Details

Training Configuration

base_model: Qwen3.5-4B
quantization: 4-bit NF4 (BitsAndBytes)
lora_rank: 16
lora_alpha: 32
lora_dropout: 0.05
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
learning_rate: 2e-4
epochs: 3
batch_size: 1
gradient_accumulation: 4
max_seq_length: 4096
optimizer: paged_adamw_8bit
scheduler: cosine
warmup_ratio: 0.1

Ecosystem

CAJAL-4B-P2PCLAW is part of a complete ecosystem:

Integration Guides

• OpenClaw
• Hermes
• Kilocode
• Codex CLI
• Cursor
• Windsurf
• LM Studio
• Ollama
• Pinokio

System Prompt

The model uses a specialized 14-step paper-writing procedure:

You are CAJAL-4B, an autonomous scientific research agent specializing in 
peer-to-peer network architectures, crypto-legal frameworks, game-theoretic 
consensus mechanisms, and distributed systems.

STEP 1: Understand the user's intent
STEP 2: Review arXiv for related work
STEP 3: Draft initial paper structure
STEP 4: Check P2PCLAW compliance
STEP 5: Enrich using APIs (Semantic Scholar, etc.)
STEP 6: Plan final paper structure
STEP 7: Verify all claims with citations
STEP 8: Suggest real data sources
STEP 9: Write test code for validation
STEP 10: Write the complete paper in LaTeX
STEP 11: Verify with Lean4 if applicable
STEP 12: Submit to P2PCLAW
STEP 13: Score and evaluate
STEP 14: Provide feedback for improvement

The full system prompt is available in cajal_9b_system_prompt.txt.

Limitations & Biases

• Trained on P2PCLAW-specific data — may not generalize well to unrelated domains
• 4-bit quantization introduces slight accuracy degradation vs full precision
• Maximum context length of 4096 tokens during training (32K at inference)
• English and Spanish primary; other languages may have reduced quality
• The model follows P2PCLAW-specific rules and constitution by design

Citation

@misc{cajal4b2026,
  title={CAJAL-4B-P2PCLAW: Autonomous Scientific Research Agent},
  author={Agnuxo},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/Agnuxo/CAJAL-4B-P2PCLAW}
}

License

Apache License 2.0 — See LICENSE for details.

Acknowledgments

• Base model: Qwen3.5-4B by Alibaba Cloud
• Training framework: Transformers + PEFT + BitsAndBytes
• P2PCLAW ecosystem: P2PCLAW