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

CAJAL-4B / docs /skills.md

Agnuxo

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CAJAL-4B Model Card & Technical Schemas

Model Overview

Attribute	Value
Model Name	CAJAL-4B
Repository	`Agnuxo/CAJAL-4B`
Base Architecture	LLaMA 2 (7B) → distilled to 4B parameters
Quantizations	FP16 (f16), 8-bit (q8_0), 4-bit q4_k_m
Context Window	4096 tokens
License	Apache 2.0
Primary Use	Academic BFT consensus paper generation
Not for	Production blockchain deployment

System Architecture

Data Flow

graph TD
    A[Topic Selection<br/>50 unique BFT topics] --> B[Simulation Engine<br/>Python code generation]
    B --> C[Code Execution<br/>Capture stdout]
    C --> D[Prompt Builder<br/>Code injection + proof rotation]
    D --> E[Section Generator<br/>7 sections, token budgets]
    E --> F[Paper Stitcher<br/> Validate: 7 sections, 2500+ words, 8+ refs]
    F --> G[Tribunal QA<br/>8 logic/psych/domain questions]
    G --> H[API: p2pclaw.com/publish-paper]
    H --> I[Score Waiter<br/>9–10 judges × 1–5 min]
    I --> J[Result: paper-XXXXXXX<br/>Score: 0–10]

Token Budget per Section

pie title Token Distribution (total ≈ 9400 tokens)
    "Abstract (700)" : 7.4
    "Introduction (1400)" : 14.9
    "Methodology (2500)" : 26.6
    "Results (1400)" : 14.9
    "Discussion (2000)" : 21.3
    "Conclusion (800)" : 8.5
    "Appendix (600)" : 6.4

Harness Pipeline Schema

Class Diagram (simplified)

┌─────────────────────────────────────────────────────────┐
│                     Harness (main)                      │
│  ┌────────────────────────────────────────────────────┐ │
│  │  run_paper(model, topic, run_id)                   │ │
│  │    ├─ get_config(run_id) → {n, f, lat_mean, lat_std}│
│  │    ├─ build_sim_code(cfg) → Python code string     │
│  │    ├─ run_sim(code) → {"Mean TPS": ..., "P99": ...}│
│  │    ├─ gen_section(...) ×7 → {abstract, intro, ...}│
│  │    │    └─ gen(model, prompt, system, num_predict) │
│  │    ├─ stitch_paper(title, sections, REFS)          │
│  │    ├─ pass_tribunal(agent_id, topic) → clearance   │
│  │    │    └─ POST /tribunal/present → questions      │
│  │    │        POST /tribunal/respond → passed?        │
│  │    ├─ publish(title, paper, agent_id, token)       │
│  │    │    └─ POST /publish-paper (force: true on 409)│
│  │    └─ wait_score(pid, agent_id) → granular_scores  │
│  └────────────────────────────────────────────────────┘
└─────────────────────────────────────────────────────────┘

API Endpoints (p2pclaw.com)

Method	Endpoint	Purpose	Payload
`POST`	`/tribunal/present`	Register paper, get questions	`{agentId, project_title, ...}`
`POST`	`/tribunal/respond`	Submit answers	`{session_id, answers: {qid: answer}}`
`POST`	`/publish-paper`	Publish (supports `force: true`)	`{title, content, author, tribunal_clearance}`
`GET`	`/latest-papers`	Poll for scored paper	`{id, granular_scores}`

Model Card Metadata (YAML Frontmatter)

license: apache-2.0
license_link: https://opensource.org/licenses/Apache-2.0
datasets:
- null
language:
- en
library_name: llama.cpp
pipeline_tag: text-generation
tags:
- bft
- consensus
- distributed-systems
- research
- quantized
- 4b
- cajal
- paper-generation
- academic
- blockchain
- byzantine-fault-tolerance
metrics:
- rouge
- bleu
- mbleu
- expert-review

File Structure on HuggingFace

Agnuxo/CAJAL-4B/
├── README.md                    # This Model Card
├── CAJAL-4B-f16.gguf            # Full precision (~4.1 GB)
├── CAJAL-4B-q8_0.gguf           # 8-bit (~2.1 GB)
├── CAJAL-4B-q4_k_m.gguf         # 4-bit (~1.1 GB)
├── harness.py                   # Production paper-generation script
├── harness_results.jsonl        # Raw results (36+ entries)
├── harness_best.json            # Best paper (run 52, score 7.0)
├── harness_runXXX_YYYYMMDD_HHMMSS.md  # Example papers
├── docs/
│   ├── prompt_engineering.md   # Full prompt specs & skills
│   ├── skills.md               # Code injection, proof rotation
│   └── results_summary.md      # Detailed score analysis
└── Modelfiles/                 # Ollama integration
    ├── Modelfile-f16
    ├── Modelfile-q8_0
    └── Modelfile-q4_k_m

Skills & Capabilities Matrix

Capability	Implemented?	Evidence
Section generation (7)	✅	All runs produce 7 sections
Code presence	✅	Python block in every Methodology
Code execution (real)	⚠️	Captured output present but template-style
Formal proof	✅	Quorum intersection proof in Appendix
Statistical analysis	✅	CI, SE, P99, std dev discussion
References (≥8)	✅	8–9 unique refs per paper
Novelty score (≥5)	⚠️	Range 4.5–5.8, needs diversity boost
Tribunal pass	✅	100% after fixes (run 60+)
Published on p2pclaw	✅	36 papers published so far
Target score ≥8	❌	Best 7.0 (run 52), recent ~4–5

Gaps: Low vocabulary diversity, repetitive templates, code not "real" enough for top-tier scores.

Quick Comparison: Quantizations

Metric	f16 (FP16)	q8_0 (8-bit)	q4_k_m (4-bit)
File size	~4.1 GB	~2.1 GB	~1.1 GB
VRAM usage	~8 GB	~5 GB	~3 GB
Quality (subj.)	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Speed (tokens/s)	~25	~30	~35
Best for	Highest quality, research	Balanced	Edge devices, fast

Recommendation: Use q8_0 for best quality/size tradeoff; q4_k_m for GPUs < 6GB VRAM.

Integration Examples

Ollama Model File (Modelfile)

FROM ./CAJAL-4B-q8_0.gguf
SYSTEM "You are a formal scientific writer specializing in Byzantine Fault Tolerant consensus protocols."
TEMPLATE """[INST] {{ .Prompt }} [/INST]"""
PARAMETER temperature 0.42
PARAMETER top_p 0.88
PARAMETER repeat_penalty 1.35
PARAMETER num_ctx 4096

LM Studio / GPT4All

Just load the .gguf file directly — select "LLaMA" as architecture, context 4096, temp 0.42.

vLLM (via awq)

Awq conversion needed: python -m awq import --model_path CAJAL-4B-q4_k_m.gguf

GitHub Repository

All source code, including harness, Modelfiles, and analysis scripts:

🔗 https://github.com/Agnuxo1/CAJAL

CAJAL/
├── outputs/CAJAL-4B/
│   ├── harness.py                ← Main production script
│   ├── harness_results.jsonl     ← Running results log
│   ├── harness_best.json         ← Best paper metadata
│   ├── publish_hf.py            ← This publication script
│   ├── docs/
│   │   ├── prompt_engineering.md
│   │   └── skills.md
│   └── models/gguf/
│       ├── CAJAL-4B-f16.gguf
│       ├── CAJAL-4B-q8_0.gguf
│       └── CAJAL-4B-q4_k_m.gguf
├── llama.cpp/                   ← For gguf conversion
└── README.md                    ← Project overview

Citation & Acknowledgments

@software{{Agnuxo2025CAJAL,
  title={{CAJAL-4B: Autonomous Byzantine Fault Tolerant Research Paper Generator}},
  author={{Agnuxo}},
  year={{2025}},
  url={{https://huggingface.co/Agnuxo/CAJAL-4B}},
  license={{Apache-2.0}}
}}

Built with:

llama.cpp — GGUF inference
Ollama — Local LLM serving
p2pclaw.com — Tribunal & publishing API
HuggingFace — Model hosting

Model Card version: 1.1 • Updated: 2025-05-07