PLATO Neural Inference Engine — fine-tuned LLM for PLATO knowledge scoring, generation, and Q&A
Project description
PLATO Neural v0.2.0
Neural inference engine for the PLATO knowledge network. Fine-tuned Qwen2.5-0.5B for tile scoring, Q&A, gap detection, and tile generation.
Install
pip install plato-neural
CLI Usage
# Ask a question
plato-neural ask "What is constraint theory?"
# Score a tile (neural — requires GPU)
plato-neural score -q "What is a tile?" -a "A 256-byte knowledge unit..."
# Score a tile (heuristic — no GPU needed)
plato-neural score -q "What is a tile?" -a "A knowledge unit..." --heuristic
# Check model health
plato-neural health
# Start REST API
plato-neural serve --port 5050
REST API
# Health
curl http://localhost:5050/health
# Ask
curl -X POST http://localhost:5050/ask -H 'Content-Type: application/json' \
-d '{"question": "What is constraint theory?"}'
# Score
curl -X POST http://localhost:5050/score -H 'Content-Type: application/json' \
-d '{"question": "...", "answer": "..."}'
# Batch score
curl -X POST http://localhost:5050/batch_score -H 'Content-Type: application/json' \
-d '{"tiles": [{"question": "...", "answer": "..."}]}'
# Generate tile
curl -X POST http://localhost:5050/generate_tile -H 'Content-Type: application/json' \
-d '{"room": "ct"}'
# Find knowledge gaps
curl -X POST http://localhost:5050/find_gaps -H 'Content-Type: application/json' \
-d '{"tiles": [...], "threshold": 50}'
Python API
from plato_neural import PlatoBrain
brain = PlatoBrain("Qwen/Qwen2.5-0.5B")
# Q&A
result = brain.ask("What is constraint theory?")
print(result["answer"])
# Score tiles
score = brain.score("What is a tile?", "A 256-byte knowledge unit...")
print(f"PPL: {score['perplexity']}, Tier: {score['tier']}")
# Generate new tiles
tile = brain.generate_tile("ct")
print(tile["question"], tile["answer"])
# Find gaps in knowledge
gaps = brain.find_gaps(tiles, threshold=50)
Performance (RTX 4050, exp8 LoRA)
| Metric | Value |
|---|---|
| Avg perplexity | ~5-7 |
| Tile recognition | ~98% |
| Confidence | 0.97+ |
| Generation speed | 70+ tok/s |
| VRAM | ~1.0GB (bf16), ~545MB (INT8) |
Changelog
v0.2.0
- REST API server with 6 endpoints
- Heuristic scorer (no GPU required)
- CLI with ask/score/health/serve commands
- PlatoBrain class with ask/score/generate/batch_score/find_gaps
- PlatoScorer class for lightweight heuristic scoring
v0.1.0
- Initial release: PlatoBrain + PlatoScorer
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
plato_neural-0.3.0.tar.gz
(8.8 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file plato_neural-0.3.0.tar.gz.
File metadata
- Download URL: plato_neural-0.3.0.tar.gz
- Upload date:
- Size: 8.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2a8032284b9e05944e74a053d9e48edf3a6d305a3cf9391d7a686850866a4496
|
|
| MD5 |
159fcbd9c58fdc9e542504e6b05ecffd
|
|
| BLAKE2b-256 |
c96270979edbbeb0c23628d4ae5006b2a193a42bb5e41e03b2d887ad99c4cd70
|
File details
Details for the file plato_neural-0.3.0-py3-none-any.whl.
File metadata
- Download URL: plato_neural-0.3.0-py3-none-any.whl
- Upload date:
- Size: 9.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2b10ffd6ccab76ad3a58d55395dd45c0bd86c118cb452ccef16887f1906b476d
|
|
| MD5 |
106c79ba6c665239d6c7714251e5c6d4
|
|
| BLAKE2b-256 |
57fe535f241d0d46ab1f5ca4d006f29b2df3b7e2697ddf630da9315a0facdd4a
|