---
base_model: CohereLabs/tiny-aya-global
library_name: peft
tags:
- lora
- tiny-aya-global
- uncertainty-calibration
- fine-tuned
license: apache-2.0
---

# tiny-aya-global Hedge-Instruction LoRA

A small LoRA adapter fine-tuned on top of [`CohereLabs/tiny-aya-global`](https://huggingface.co/CohereLabs/tiny-aya-global) to fix one specific,
verified failure mode: the base model ignoring an explicit "if unsure, say
I'm not sure" instruction and answering confidently instead.

This adapter was built as a follow-up to a qualitative blind-spot evaluation
of the base model: [`kelvinyelyen/tiny-aya-global-blindspots`](https://huggingface.co/datasets/kelvinyelyen/tiny-aya-global-blindspots).
One finding there was that, given a prompt that explicitly instructed the
model to hedge if uncertain, the base model answered a stale fact (a former
president of Ghana) with full confidence, ignoring the instruction outright.
This adapter targets that specific behavior.

## Method

- **Base model:** `CohereLabs/tiny-aya-global`, loaded in 4-bit (`bitsandbytes`, NF4).
- **Adapter:** LoRA, rank 8, alpha 16,
  applied to: k_proj, o_proj, q_proj, v_proj (detected at runtime from the base
  model's actual module names, not assumed from a different model family).
- **Training data:** 15 hand-built examples (10 pairing an explicit hedge instruction with a response that actually
hedges, 5 plain factual control questions with confident correct
answers). The control examples exist so the adapter learns *selective*
hedging tied to genuine uncertainty, not "always hedge" regardless of the
question.
- **Training:** 3 epochs, batch size
  2 (grad accumulation
  4), learning rate
  0.0002.

## Results

*(Fill in from the `before_after` list printed in Section 8 after running the
notebook — not pre-filled here, since these are actual model outputs that
need to be observed, not assumed.)*

| Prompt | Before | After |
|---|---|---|
| ... | ... | ... |

Also report here whether the adapter hedged appropriately on the plain factual
control prompts (it shouldn't) — that's the real test of whether this
generalized rather than overfit to "always say I'm not sure."

## Limitations

- Trained on 15 examples — small enough that
  memorization of the exact training phrasing is a real risk. Check the
  training loss curve and the control-prompt behavior before trusting this
  as a general fix.
- Targets one failure mode only (ignored hedge instructions). Says nothing
  about the model's other documented blind spots (exact counting, low-resource
  translation, formal logical inference) — see the blind-spot dataset above
  for those.
- Single training run, no held-out validation set beyond the 5
  qualitative examples in Section 8.

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-global", device_map="auto")
model = PeftModel.from_pretrained(base, "kelvinyelyen/tiny-aya-global-hedge-lora")
tokenizer = AutoTokenizer.from_pretrained("kelvinyelyen/tiny-aya-global-hedge-lora")
```