harrier77/LFM2.5-1.2B-ITA-GGUF

LFM2.5-1.2B Italian

LiquidAI/LFM2.5-1.2B-Base model further continued-pretrained on Italian Wikipedia and then fine-tuned with Italian instructions (Alpaca format).

Training

• Continued Pretraining: ~1% of Italian Wikipedia (20231101.it dump), texts ≥ 600 characters
• Instruction Tuning: dataset DanielSc4/alpaca-cleaned-italian
• Technique: LoRA + Unsloth (merged in full precision / fp16)
• Original base model: multilingual, with significant improvement in Italian

Supported Languages

Improved in Italian.
Maintains performance in English and residual capability in the other languages of the base model.

This model follows the exact continued pretraining + instruction tuning recipe recently published by LiquidAI in their official cookbook: https://github.com/Liquid4All/cookbook/blob/main/finetuning/notebooks/cpt_translation_with_unsloth.ipynb I only replaced Korean with Italian by using: Italian Wikipedia (20231101.it dump) instead of Korean Wikipedia for continued pretraining DanielSc4/alpaca-cleaned-italian dataset instead of the Korean Alpaca version for instruction tuning All other steps (LoRA including embed_tokens & lm_head, separate embedding learning rate, Unsloth, fp16 merge) are identical to the original notebook.