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Last updated: 2026-08-24
## Summary
TAYI is a working local research prototype for a consumer-GPU coding agent. It currently has:
- a Rust CLI for indexing, retrieval, context construction, solving, patch validation, and test execution;
- a Python research package for evaluation, distillation, teacher repair, capability analysis, and adapter training flows;
- local gold fixtures across Python, JavaScript, Rust, Go, and Java;
- scripts for workspace-local environment setup, `llama.cpp` serving, open-teacher serving, WSL training, and cleanup;
- documented v3 context/curriculum and v5 system-optimization research tracks.
The repo is ready to be saved to GitHub as source and documentation. Large generated assets stay local and are restored separately.
## Status
Current status is research prototype, not production model release.
Working:
- CLI command surface builds and exposes `index`, `analyze`, `retrieve`, `context`, `solve`, `apply-patch`, `test`, `doctor`, and `local-check`.
- Workspace-local Cargo target/cache policy is configured through `.cargo/config.toml`.
- Python tests cover the research modules.
- Local model and teacher paths are standardized under `model/`.
- Large files are excluded by `.gitignore`.
- README and restore docs explain how to hand off the project.
Known limits:
- v5.1 semantic adapter was not promoted because target3 stayed at `2/3`.
- Current verified semantic repair data is too small for a meaningful capability jump.
- Full broad-language claims require more fixtures and verified repairs.
- Large local assets are not in Git and must be re-created or preserved outside the repository.
## Measured Capability
Known local readout from the existing project notes:
| System | Eval slice | Result |
| --- | --- | --- |
| Base 7B HF student | local gold-20 | `13/20`, pass@1 `0.65` |
| Early TAYI LoRA v0 | local gold-20 | `14/20`, pass@1 `0.70` |
| Runtime v4.4 | target3 | `2/3`, pass@1 `0.6667` |
| v5 system adapter | target3 | `2/3`, pass@1 `0.6667` |
| v5.1 semantic adapter | target3 | `2/3`, pass@1 `0.6667`, not promoted |
Interpretation:
- TAYI has a real repo-level loop and measurable progress.
- It should be described as a promising local coding-agent system, not as a finished frontier-class model.
- The next bottleneck is verified semantic repair data scale and harder fixtures.
## Hardware Policy
Primary target:
- RTX 5060 Ti 16GB or similar consumer GPU.
Policy:
- hard VRAM ceiling: 16GB;
- preferred active band: 8-12GB for routine work;
- training and serving should reduce context, batch size, rank, or offload before exceeding the ceiling;
- use retrieval and context compression instead of brute-force full-repo context.
## Git Tracking Policy
Track:
- source code;
- tests;
- scripts;
- configs;
- docs;
- brand assets;
- small example fixtures;
- schemas and small task definitions.
Do not track:
- `model/`;
- `artifacts/`;
- `.cache/`;
- `third_party/`;
- `downloads/`;
- `.wsl/`;
- `.aicoder/` and `.tayi/` runtime traces;
- checkpoint, GGUF, safetensors, or temporary training output files.
## Next Mainline
Next milestone:
```text
verified_semantic_repair_pack_v1
```
Definition:
- build 20-50 verified semantic-repair samples;
- include source context, paired specs/tests, failed candidates, logs, and passing patches;
- expand fixture coverage beyond the current small local set;
- run teacher repair only through isolated verification;
- train v5.2 with more than an 8-step smoke only after the verified pack exists;
- promote only through fixed quality gates.
## Recommended Next Commands
```powershell
. .\scripts\tayi_env.ps1
cargo test
$env:PYTHONPATH="src_py"; python -m unittest discover -s tests_py
```
Then generate or refresh the next training/eval artifacts:
```powershell
$env:PYTHONPATH="src_py"; python -m aicoder_research.system_optimizer --help
$env:PYTHONPATH="src_py"; python -m aicoder_research.gold_eval --help
$env:PYTHONPATH="src_py"; python -m aicoder_research.failure_refinery --help
```
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