RoLlama-3.2-1B is a continually-pretrained adaptation of meta-llama/Llama-3.2-1B for the Romanian language, produced under a constrained compute budget (single RTX 3090). It was trained with QLoRA on 2.4B Romanian tokens (FineWeb2-Edu-Ro, quality-filtered) mixed with English (80/20) to limit catastrophic forgetting.

Designed for 4-bit (QLoRA) inference under constrained VRAM. The adapter was trained on a 4-bit (nf4) base and is intended to run in that configuration. Reported metrics use 4-bit inference — its deployment precision.

Model Details

Model Description

Developed by: OpenLLM-Ro
Language(s): Romanian (ro), English (en)
License: Llama 3.2 Community License
Continually pretrained from: meta-llama/Llama-3.2-1B
Training corpus: FineWeb2-Edu-Ro (educational quality score = 4 subset) + FineWeb (EN), 80/20 mix, 2.4B tokens
Method: QLoRA (4-bit), LoRA rank 64 incl. embed_tokens + lm_head, WSD scheduler, no sequence packing

Model Sources

Repository: https://github.com/pariidanDKE/TrainingRoLlama3.2-1B
Write-up: https://dan1180627.substack.com/p/rollama32-1b-cpt-of-a-small-language

Intended Use

Intended Use Cases

Research on Romanian language adaptation and continual pretraining of small models. This is a base model (not instruction-tuned); it is intended for further fine-tuning (SFT/DPO) or text-completion research, not for direct chat/instruction following.

Out-of-Scope Use

Any use that violates the Llama 3.2 license or applicable laws; use in languages other than Romanian/English; deployment as an assistant without further alignment.

How to Get Started with the Model

This is a LoRA adapter meant to be loaded on the base model in 4-bit (its training/deployment config):

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model = "meta-llama/Llama-3.2-1B"
adapter = "OpenLLM-Ro/RoLlama-3.2-1B"

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tok = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

inputs = tok("Capitala României este", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=40)
print(tok.decode(out[0], skip_special_tokens=True))

Academic Benchmarks

Accuracy (%), 4-bit inference, averaged across the standard few-shot settings. Higher is better. Average is the mean over all six tasks.

Model	Average	ARC	MMLU	Winogrande	HellaSwag	GSM8k	TruthfulQA
Llama-3.2-1B (base)	31.25	29.45	24.50	51.82	35.75	1.21	44.76
RoLlama-3.2-1B	32.55	31.33	23.59	54.14	40.21	0.18	45.82

_{ARC/HellaSwag report acc_norm; MMLU/Winogrande/TruthfulQA report acc; GSM8k reports exact_match. Scores from the final 2.4B-token checkpoint of the FineWeb2-Edu-Ro run, 4-bit inference (deployment precision).}

Downstream Tasks

LaRoSeDa (sentiment) — Macro F1

Model	Binary (Few-shot)	Binary (Finetuned)	Multiclass (Few-shot)	Multiclass (Finetuned)
Llama-3.2-1B (base)	50.84	-	33.42	-
RoLlama-3.2-1B	67.54	-	25.63	-

WMT (translation) — BLEU

Model	EN→RO (Few-shot)	EN→RO (Finetuned)	RO→EN (Few-shot)	RO→EN (Finetuned)
Llama-3.2-1B (base)	6.11	-	15.60	-
RoLlama-3.2-1B	2.10	-	2.94	-

XQuAD (extractive QA)

Model	EM (Few-shot)	F1 (Few-shot)	EM (Finetuned)	F1 (Finetuned)
Llama-3.2-1B (base)	20.88	31.11	-	-
RoLlama-3.2-1B	13.51	25.21	-	-

STS (semantic textual similarity)

Model	Spearman (Few-shot)	Pearson (Few-shot)	Spearman (Finetuned)	Pearson (Finetuned)
Llama-3.2-1B (base)	0.019	0.018	-	-
RoLlama-3.2-1B	-0.004	-0.005	-	-

Additional Romanian Signals (supplementary, not in standard suite)

Metric	Llama-3.2-1B (base)	RoLlama-3.2-1B	Note
RoWiki perplexity ↓	60.44	32.47	primary Romanian fluency signal
RO Belebele (acc_norm)	26.47	27.22	reading comprehension
RO Grammar (acc)	28.37	27.74

English Retention (catastrophic forgetting check)

Metric	Llama-3.2-1B (base)	RoLlama-3.2-1B
WikiText perplexity ↓	12.35	14.87
ARC-Challenge (acc_norm)	34.64	33.70
Winogrande (acc)	61.25	59.67

Training Recipe (summary)

Hyperparameter	Value
LoRA rank / alpha	64 / 64
Target modules	attn + MLP + `embed_tokens` + `lm_head`
Learning rate / embedding LR	1e-4 / 2e-5
Effective batch size	128 (BS 4 × GA 32)
Data mix	80% RO (FineWeb2-Edu-Ro) / 20% EN
Sequence packing	disabled
Scheduler	warmup_stable_decay (6% warmup, 5% decay)
Precision	BF16 + QLoRA 4-bit + FlashAttention 2 + grad checkpointing
Tokens	2.4B (8 milestones × 300M)

Citation

bibtex

@misc{parii2026rollama32,
  title  = {RoLlama-3.2-1B: Continual Pretraining of a Small Language Model for Romanian under Compute Constraints},
  author = {Parii, Dan},
  year   = {2026},
  howpublished = {\url{https://dan1180627.substack.com/p/rollama32-1b-cpt-of-a-small-language}}
}