kelvinyelyen

tiny-aya-global-hedge-lora

README

License: apache-2.0

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.)

Table with columns: Prompt, Before, After
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 PeftModelfrom 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")

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Model Details

Model Provider

kelvinyelyen

Model Tree

Base

CohereLabs/tiny-aya-global

Adapter

this model

Input Modalities

Text

Output Modalities