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language:
- id
- en
license: mit
library_name: pytorch
tags:
- diffusion
- text-generation
- aam
- aphantasic-abstraction-model
- sentence-arrangement
- graph-conditioned
---
# AAM Diffusion LLM v1.0
> **"AAM = 1 Pikiran + 1 Tubuh" (1 Mind + 1 Body)**
The dedicated "body" of the Aphantasic Abstraction Model (AAM) — a small diffusion LLM specifically trained to arrange sentences from structured graph data.
## What is this?
This is NOT a general-purpose LLM. This is a SPECIALIZED sentence composer that:
- Takes **graph-structured conditioning** as input (evidence, anomalies, reasoning chains, confidence scores)
- Produces **coherent natural language narratives** through iterative denoising
- **Cannot hallucinate** — it can only narrate what the graph knows
## Architecture
```
Graph Conditioning Encoder → Diffusion Transformer → Noise Scheduler
(Mind input) (The Body) (Iterative refinement)
```
### Key Components
- **Graph Conditioning Encoder**: Encodes evidence nodes, compositions, anomalies, reasoning chains with confidence and temporal embeddings
- **Diffusion Transformer**: Core denoising network with adaptive layer norm, self-attention, and cross-attention to graph conditioning
- **Noise Scheduler**: Cosine noise schedule with DDPM/DDIM sampling support
## Model Details
| Parameter | Value |
|-----------|-------|
| Architecture | Diffusion Transformer |
| d_model | 256 |
| n_layers | 4 |
| n_heads | 4 |
| d_ff | 1024 |
| Parameters | 73.4M |
| Vocab size | 576 |
| Max sequence length | 128 |
| Diffusion timesteps (train) | 200 |
| Diffusion timesteps (inference) | 20 |
| Noise schedule | cosine |
| Prediction type | epsilon |
| Sampling method | ddim |
## Usage
```python
from diffusion_llm import AamDiffusionModel, AamTokenizer, AamGenerator, AamDiffusionConfig
# Load model
config = AamDiffusionConfig.from_json("config.json")
model = AamDiffusionModel.load("model.pt")
tokenizer = AamTokenizer.load("tokenizer.json")
# Create generator
generator = AamGenerator(model, tokenizer, config)
# Generate narrative from graph conditioning
result = generator.generate(
trigger="Siapa yang mencuri Snow Plum Pill?",
evidence_nodes=["Hefei", "Diancang Five Swords", "Ju Jangmok"],
anomalies=["Tidak ada konsumsi pil baru di pasar gelap"],
reasoning_steps=["Cross-reference tanggal kejadian"],
source_trust=0.85,
)
print(result.narrative)
```
## Philosophy
**AAM = 1 Mind + 1 Body**
- **Mind** = RSVS Knowledge Graph (structural memory, perfect recall, relational understanding)
- **Body** = This Diffusion LLM (sentence arranger, graph-conditioned, anti-hallucination)
Unlike using a rented LLM (GPT, Claude) as the "body", this model is specifically trained for AAM:
- It cannot generate information not present in the graph conditioning
- It arranges sentences based on structured evidence
- It uses diffusion (non-sequential generation) instead of autoregressive generation
- It is small (73.4M) but specialized
## Training
Trained on synthetic Graph→Narrative pairs with:
- Indonesian and English narrative templates
- Evidence nodes, anomalies, reasoning chains
- Confidence score distributions
- Source trust scores
## License
MIT
|