llama32-3B-sst2-int8-lora-seed43

LoRA adapter trained on GLUE SST2 on top of a int8 backbone of meta-llama/Llama-3.2-3B.

Part of a controlled study of whether the backbone bit-width changes what a LoRA adapter learns. For a given (model size, seed) the adapter initialisation is identical across the bf16 / int8 / nf4 arms, and the data order, optimiser, schedule and LoRA hyperparameters are held fixed — so any difference in the learned update is attributable to the backbone.

Result

metric	validation	test
accuracy	0.9630	0.9679
macro-F1	0.9625	0.9679
loss	0.1273	0.1199

Test-set majority-class baseline: 0.5092

• peak GPU memory: 5.59 GiB
• training time: 85.0 min (936 steps)
• GPU: NVIDIA GeForce RTX 4090

Setup

• seed: 43 · adapter init: shared:lora_init_3B_seed43.pt:224tensors
• LoRA: r=16, alpha=32, dropout=0.0, bias=none, target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj']
• trainable params: 9,175,040
• epochs 3, lr 0.0002, max_len 256, batch 4 x grad_accum 16, cosine schedule, warmup 0.03

Prompt format

Trained as causal LM with the loss on the answer letter only (prompt tokens masked to -100):

Sentence: ...

Is the sentiment of this sentence positive or negative?

A. Negative
B. Positive

Answer:

Evaluated by conditional likelihood over the answer letters (Negative, Positive).

GLUE test is unlabeled, so the official validation split is used as TEST and the validation set is carved from train (disjoint).