| --- |
| language: en |
| license: other |
| base_model: Qwen/Qwen2.5-3B-Instruct |
| tags: |
| - vyasa |
| - architectural-reasoning |
| - structured-generation |
| - code-generation |
| - lora |
| - peft |
| datasets: |
| - prashantpandey/vyasa-architectural-pairs |
| metrics: |
| - valid-json-rate |
| - witness-clean-rate |
| --- |
| |
| # Vyasa-Architect-3B |
|
|
| A 3B-parameter language model fine-tuned for architectural reasoning — converting natural language app descriptions into complete, structured Architectural Graph JSON. |
|
|
| Named after Vyasa (व्यास), the sage who conceived the entire Mahabharata completely in his mind before manifesting a single word. |
|
|
| ## Model Description |
|
|
| - **Base Model:** Qwen-2.5-3B-Instruct (4-bit quantized) |
| - **Fine-tuning Method:** QLoRA (LoRA rank 8, 8 layers, 3.33M trainable params) |
| - **Training Data:** 167 (intent → Architectural Graph) pairs generated by DeepSeek-V3 |
| - **Training Hardware:** Apple M1 Pro, 32GB unified memory |
| - **Training Duration:** ~1 hour, 300 iterations |
| - **Training Loss:** 0.063 (train), 0.124 (validation) |
|
|
| ## Intended Use |
|
|
| This model is the Stage 1 (Conception) component of the Vyasa Manifestation Engine. It receives a natural language app description and produces a complete Architectural Graph JSON. This graph is then validated by a deterministic Witness (12 structural checks) and compiled to working code files by the Manifest Engine (deterministic Next.js/Prisma/TypeScript compiler). |
|
|
| The model does NOT generate code directly. It generates architectural specifications. Code is produced deterministically by the compiler. |
|
|
| ## Performance |
|
|
| | Metric (n=50, preregistered protocol) | Base Qwen-2.5-3B | Vyasa-Architect-3B (this adapter) | |
| |--------|------------------|--------------------| |
| | Parseable JSON | 62% | 40% | |
| | Schema-valid graph | 8% | 38% | |
| | Witness-clean | 6% [2.1–16.2] | 14% [7.0–26.2] | |
|
|
| Measured 2026-07-03 (greedy decoding, Wilson 95% CIs). Fine-tuning teaches |
| schema conformance (8% → 38%) and structural discipline (6% → 14%), not JSON |
| syntax — the untrained base already parses 62% of the time. A successor trained |
| on concise pairs (arch-v2 recipe) measures 34% Witness-clean, 52% after a |
| deterministic Witness-guided repair pass. GRPO+Witness training is gated on a |
| preregistered pass@8 headroom probe. |
|
|
| ## Usage |
|
|
| ```python |
| from mlx_lm import load, generate |
| |
| model, tokenizer = load( |
| "mlx-community/Qwen2.5-3B-Instruct-4bit", |
| adapter_path="prashantpandey/vyasa-architect-3b" |
| ) |
| |
| prompt = "Conceive the complete architecture for: A todo app with tasks and categories" |
| response = generate(model, tokenizer, prompt=prompt, max_tokens=3000) |
| ``` |
|
|
| ## Architecture |
|
|
| Part of the Vyasa Manifestation Engine — a three-stage compiler architecture: |
| 1. **Conception** (this model): Intent → Architectural Graph |
| 2. **Witness** (deterministic): Validates structural integrity |
| 3. **Manifest** (deterministic): Compiles graph to code files |
|
|
| ## Limitations |
|
|
| - 40% parseable / 14% Witness-clean at n=50 (see Performance) — verbosity-truncation at long outputs is the dominant failure |
| - Output is verbose (8K-11K chars per graph) |
| - Slow on CPU (~200 chars/sec); optimized for GPU inference |
| - Trained for Next.js/Prisma stack output; graph format is stack-agnostic |
|
|
| ## Citation |
|
|
| If you use this model, please cite: |
| ``` |
| @misc{vyasa-architect-3b, |
| author = {Prashant Pandey}, |
| title = {Vyasa-Architect-3B: Architectural Reasoning via Structured Intermediate Representation}, |
| year = {2026}, |
| url = {https://github.com/prashantpandey-creator/puranic-architecture-paper} |
| } |
| ``` |
|
|
| ## License |
|
|
| This adapter is proprietary. All rights reserved. The base model (Qwen-2.5-3B) is Apache 2.0. |
|
|
