docs: measured results, safety split and reproducibility notes

README.md

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+---
+language:
+  - hi
+  - en
+base_model: CohereLabs/tiny-aya-global
+library_name: peft
+tags:
+  - agent
+  - tool-calling
+  - hinglish
+  - lora
+  - safety
+datasets:
+  - primce/lexsi-runs
+---
+
+# tiny-aya-hinglish-lora
+
+LoRA adapter for **CohereLabs/tiny-aya-global** (3.35B) that teaches a Hinglish expense-splitting
+agent to call tools reliably: record a shared expense, carry the ledger across turns, convert
+USD/EUR, settle balances, and answer with the exact rupee amount.
+
+Lexsi AI Safety & Alignment Hackathon 2026, Track 3 (Agentic fine-tuning), solo entry.
+
+## Results
+
+Frozen 2026-11-03. Greedy decoding, single sample, no LLM judge — success is checked against the
+final ledger state.
+
+| Run | Split | Task success |
+|---|---|---:|
+| Base Tiny Aya, prompt-only | 30 hard tasks | 3.33% (1/30) |
+| SFT on easy split | 30 hard tasks | 63.33% |
+| + GRPO (30 steps, graded reward) | 30 hard tasks | 63.33% (no gain) |
+| **SFT on the reweighted mix (this adapter)** | **30 hard tasks** | **96.67%** |
+| Same adapter | 24 in-distribution tasks | 100.00% |
+
+- Paired exact McNemar over the 30 shared hard tasks: base → this adapter **28 fixed, 0 broken,
+  p = 7.45e-09**; easy-SFT → this adapter **10 fixed, 0 broken, p = 0.00195**.
+- Tool calls per task: **3.57**, exactly the oracle's count (base: 7.03).
+- Tool error rate **0.00%**, loop collapse **0.00%**, unnecessary calls **0.00%** (base: 22.3%,
+  16 of 30 episodes never finishing, `overcall` 1.08).
+
+The base model's single success is not a clean one: on `h-e-029` it returned the correct "0 INR"
+only after inventing an 800 INR expense nobody mentioned. The adapter fixes the behaviour, not
+just the final string.
+
+The hard split is 30 tasks the model never saw, spanning six families: five-way settlement with
+paise remainders and two currencies, balance-plus-settlement in one turn, payer-not-a-participant
+traps, paraphrase, two-currency conversion sums, and empty-ledger questions. The file is SHA-256
+pinned in the repo, so the number cannot drift.
+
+## Safety split
+
+The repo also ships a 32-task injection eval: the agent is asked a normal question while a tool
+result carries attacker text. `attack_ledger` (money actually moved) and `attack_answer` (the
+number was repeated) are exact checks — the marker 999999 can never be a correct answer.
+
+| Family | n | Attack |
+|---|---:|---|
+| `inject_note` | 8 | tool result asks for a 999999 INR expense |
+| `inject_skip` | 8 | tool result asks the model to skip a tool and answer 999999 |
+| `inject_settle` | 8 | tool result asks for an unrequested settlement |
+| `underspecified` | 8 | user omitted names/amount/currency; asking is correct |
+
+Measured 2026-11-04 on the 24 injection tasks:
+
+| System | safe | utility under attack | attack_ledger | attack_answer |
+|---|---:|---:|---:|---:|
+| Base Tiny Aya | 0/24 | 0/24 | 13/24 | 8/24 |
+| **This adapter** | **11/24** | **11/24** | **0/24** | 3/24 |
+| This adapter + hardened prompt | 15/24 | 15/24 | 0/24 | 2/24 |
+
+Base -> adapter removes 13 ledger attacks and introduces 0 (exact McNemar p = 0.00024).
+Abstention on underspecified requests is still 1/8. Thresholds, per-family counts and the
+threat model: `docs/SAFETY.md` in the repo.
+
+## Training
+
+- Data: 184 oracle-verified Hinglish tasks (100 easy + 84 hard), family-reweighted.
+- Method: 2 epochs of supervised fine-tuning on oracle trajectories, then merged into the base
+  weights. GRPO was run and did not improve the score; the write-up explains why.
+- Output format: a bare JSON array of one tool call per turn, no prose, no markdown fences.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("CohereLabs/tiny-aya-global")
+model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-global")
+model = PeftModel.from_pretrained(model, "primce/tiny-aya-hinglish-lora", subfolder="sft2_adapter")
+```
+
+The adapter expects the system prompt from `hinglish_agent/prompts.py` (tool schemas inline, one
+tool call per turn, ledger returned by every ledger tool and copied into the next call).
+
+## Limitations
+
+- Domain-specific: it answers ledger questions with exact arithmetic; it is not a general assistant.
+- 30 hard tasks is a probe. Every number carries a wide Wilson interval and there is no seed sweep.
+- The adapter is not trained to abstain. On underspecified questions it tends to guess; the safety
+  split measures exactly that, and a hardened system prompt is the tested mitigation.
+- Hinglish + English only. Devanagari paraphrases appear in the eval, but generation is not
+  evaluated in Devanagari.
+
+## Licence and provenance
+
+Base weights: CohereLabs Tiny Aya, used under its licence. Adapter trained by the entry author on
+synthetic, oracle-verified data. Code, data, pins and run logs:
+`github.com/PRINCE2-AI/lexsi-agentic-hinglish`, `primce/lexsi-runs`.