Malaysian gemma-3-1b-it

Improvement

1. Support respond in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu.
2. Able to code in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu.
3. Multi-turn Malaysian context such as related to Malaysian Legislation, politics, religions and languages.

Training session

How we train

1. LoRA on ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "embed_tokens", "lm_head"].
2. 128 Rank with alpha 256, or alpha of 2.0
3. Multipacking 8192 context length with proper SDPA causal masking to prevent document contamination and also make sure proper position ids.
4. Chunk CCE loss for LoRA.
5. WanDB at https://wandb.ai/huseinzol05/lora-embedding-128-gemma3-1b-malaysian-8k?nw=nwuserhuseinzol05

Benchmark

MalayMMLU

                     Model   Accuracy   shot by_letter        category
0  Malaysian-gemma-3-1b-it  48.096603  0shot      True            STEM
1  Malaysian-gemma-3-1b-it  47.423664  0shot      True        Language
2  Malaysian-gemma-3-1b-it  47.210176  0shot      True  Social science
3  Malaysian-gemma-3-1b-it  47.709283  0shot      True          Others
4  Malaysian-gemma-3-1b-it  51.786121  0shot      True      Humanities
{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
Model : Malaysian-gemma-3-1b-it
Metric : first
Shot : 0shot
average accuracy 48.27158964192789
accuracy for STEM 48.09660253786328
accuracy for Language 47.4236641221374
accuracy for Social science 47.21017635154669
accuracy for Others 47.70928280163108
accuracy for Humanities 51.786120591581344

Acknowledgement