| --- |
| language: |
| - ms |
| - en |
| - zh |
| - ta |
| --- |
| |
| # Malaysian Llama-3.2-3B-Instruct |
|
|
| Continue finetuning https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct on highly curated 1.5B tokens Malaysian instruction dataset. |
|
|
| ## 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 |
|
|
| Finetune on [mesolitica/Malaysian-SFT](https://huggingface.co/datasets/mesolitica/Malaysian-SFT) to make the model understand Malaysian context. |
| |
| ## 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-llama3.2-3b-malaysian-8k?nw=nwuserhuseinzol05 |
|
|
| Source code at https://github.com/mesolitica/malaya/tree/master/session/llama3 |
|
|
| ## Benchmark |
|
|
| ### MalayMMLU |
|
|
| #### Probability next tokens |
|
|
| Based on 0-shot official MalayMMLU First token accuracy, |
|
|
| ``` |
| Model Accuracy shot by_letter category |
| 0 Malaysian-Llama-3.2-3B-Instruct 57.634056 0shot True STEM |
| 1 Malaysian-Llama-3.2-3B-Instruct 59.351145 0shot True Language |
| 2 Malaysian-Llama-3.2-3B-Instruct 57.559988 0shot True Social science |
| 3 Malaysian-Llama-3.2-3B-Instruct 57.303910 0shot True Others |
| 4 Malaysian-Llama-3.2-3B-Instruct 60.022753 0shot True Humanities |
| {'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443} |
| Model : Malaysian-Llama-3.2-3B-Instruct |
| Metric : first |
| Shot : 0shot |
| average accuracy 58.43555115020857 |
| accuracy for STEM 57.63405648792468 |
| accuracy for Language 59.35114503816794 |
| accuracy for Social science 57.55998843596415 |
| accuracy for Others 57.30390981050611 |
| accuracy for Humanities 60.02275312855517 |
| ``` |
|
|
| While the original model, |
|
|
| ``` |
| Model Accuracy shot by_letter category |
| 0 Llama-3.2-3B-Instruct 56.733524 0shot True STEM |
| 1 Llama-3.2-3B-Instruct 58.460560 0shot True Language |
| 2 Llama-3.2-3B-Instruct 54.206418 0shot True Social science |
| 3 Llama-3.2-3B-Instruct 52.554569 0shot True Others |
| 4 Llama-3.2-3B-Instruct 60.659841 0shot True Humanities |
| {'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443} |
| Model : Llama-3.2-3B-Instruct |
| Metric : first |
| Shot : 0shot |
| average accuracy 56.453145004749516 |
| accuracy for STEM 56.73352435530086 |
| accuracy for Language 58.460559796437664 |
| accuracy for Social science 54.20641803989592 |
| accuracy for Others 52.554569441112974 |
| accuracy for Humanities 60.659840728100114 |
| ``` |
|
|
| #### First token match using vLLM |
|
|
| Based on 0-shot exact first token match using vLLM, |
|
|
| ``` |
| Model Accuracy shot category |
| 0 Malaysian-Llama-3.2-3B-Instruct 51.944331 0 STEM |
| 1 Malaysian-Llama-3.2-3B-Instruct 50.795165 0 Language |
| 2 Malaysian-Llama-3.2-3B-Instruct 52.732003 0 Social science |
| 3 Malaysian-Llama-3.2-3B-Instruct 52.026865 0 Others |
| 4 Malaysian-Llama-3.2-3B-Instruct 54.539249 0 Humanities |
| Model : Malaysian-Llama-3.2-3B-Instruct |
| Metric : full |
| Shot : 0 |
| average accuracy 52.35617230413414 |
| accuracy for STEM 51.94433074089234 |
| accuracy for Language 50.795165394402034 |
| accuracy for Social science 52.73200346921075 |
| accuracy for Others 52.02686495562485 |
| accuracy for Humanities 54.53924914675768 |
| ``` |
|
|
| While the original model, |
|
|
| ``` |
| Model Accuracy shot category |
| 0 Llama-3.2-3B-Instruct 50.511666 0 STEM |
| 1 Llama-3.2-3B-Instruct 49.825064 0 Language |
| 2 Llama-3.2-3B-Instruct 48.352125 0 Social science |
| 3 Llama-3.2-3B-Instruct 48.213001 0 Others |
| 4 Llama-3.2-3B-Instruct 51.990899 0 Humanities |
| Model : Llama-3.2-3B-Instruct |
| Metric : full |
| Shot : 0 |
| average accuracy 49.58906372609755 |
| accuracy for STEM 50.51166598444535 |
| accuracy for Language 49.82506361323155 |
| accuracy for Social science 48.35212489158716 |
| accuracy for Others 48.21300071959703 |
| accuracy for Humanities 51.990898748577926 |
| ``` |
|
|
| ## Acknowledgement |
|
|
| Special thanks to https://www.sns.com.my and Nvidia for 8x H100 node! |
