| # 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 |
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|
| ```mermaid |
| 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 |
|
|
| ```mermaid |
| 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 |
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|
| ### Class Diagram (simplified) |
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|
| ``` |
| ┌─────────────────────────────────────────────────────────┐ |
| │ 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) |
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|
| | 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) |
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|
| ```yaml |
| 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 |
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|
| ``` |
| 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 |
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|
| | 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 | |
|
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| **Gaps:** Low vocabulary diversity, repetitive templates, code not "real" enough for top-tier scores. |
|
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| --- |
|
|
| ## Quick Comparison: Quantizations |
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|
| | 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. |
|
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| --- |
|
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| ## Integration Examples |
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| ### Ollama Model File (Modelfile) |
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| ```dockerfile |
| 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. |
|
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| ### vLLM (via awq) |
| Awq conversion needed: `python -m awq import --model_path CAJAL-4B-q4_k_m.gguf` |
|
|
| --- |
|
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| ## GitHub Repository |
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| All source code, including harness, Modelfiles, and analysis scripts: |
|
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| **🔗 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 |
| ``` |
|
|
| --- |
|
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| ## Citation & Acknowledgments |
|
|
| ```bibtex |
| @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](https://github.com/ggerganov/llama.cpp) — GGUF inference |
| - [Ollama](https://ollama.ai) — Local LLM serving |
| - [p2pclaw.com](https://p2pclaw.com) — Tribunal & publishing API |
| - [HuggingFace](https://huggingface.co) — Model hosting |
|
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| --- |
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| *Model Card version: 1.1 • Updated: 2025-05-07* |
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