125 lines
4.6 KiB
Markdown
125 lines
4.6 KiB
Markdown
---
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language:
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- ms
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- en
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- zh
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- ta
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---
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# Malaysian Llama-3.2-3B-Instruct
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Continue finetuning https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct on highly curated 1.5B tokens Malaysian instruction dataset.
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## Improvement
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1. Support respond in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu.
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2. Able to code in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu.
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3. Multi-turn Malaysian context such as related to Malaysian Legislation, politics, religions and languages.
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## Training session
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Finetune on [mesolitica/Malaysian-SFT](https://huggingface.co/datasets/mesolitica/Malaysian-SFT) to make the model understand Malaysian context.
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## How we train
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1. LoRA on `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "embed_tokens", "lm_head"]`.
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2. 128 Rank with alpha 256, or alpha of 2.0
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3. Multipacking 8192 context length with proper SDPA causal masking to prevent document contamination and also make sure proper position ids.
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4. Chunk CCE loss for LoRA.
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5. WanDB at https://wandb.ai/huseinzol05/lora-embedding-128-llama3.2-3b-malaysian-8k?nw=nwuserhuseinzol05
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Source code at https://github.com/mesolitica/malaya/tree/master/session/llama3
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## Benchmark
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### MalayMMLU
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#### Probability next tokens
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Based on 0-shot official MalayMMLU First token accuracy,
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```
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Model Accuracy shot by_letter category
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0 Malaysian-Llama-3.2-3B-Instruct 57.634056 0shot True STEM
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1 Malaysian-Llama-3.2-3B-Instruct 59.351145 0shot True Language
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2 Malaysian-Llama-3.2-3B-Instruct 57.559988 0shot True Social science
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3 Malaysian-Llama-3.2-3B-Instruct 57.303910 0shot True Others
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4 Malaysian-Llama-3.2-3B-Instruct 60.022753 0shot True Humanities
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{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
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Model : Malaysian-Llama-3.2-3B-Instruct
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Metric : first
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Shot : 0shot
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average accuracy 58.43555115020857
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accuracy for STEM 57.63405648792468
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accuracy for Language 59.35114503816794
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accuracy for Social science 57.55998843596415
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accuracy for Others 57.30390981050611
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accuracy for Humanities 60.02275312855517
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```
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While the original model,
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```
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Model Accuracy shot by_letter category
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0 Llama-3.2-3B-Instruct 56.733524 0shot True STEM
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1 Llama-3.2-3B-Instruct 58.460560 0shot True Language
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2 Llama-3.2-3B-Instruct 54.206418 0shot True Social science
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3 Llama-3.2-3B-Instruct 52.554569 0shot True Others
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4 Llama-3.2-3B-Instruct 60.659841 0shot True Humanities
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{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
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Model : Llama-3.2-3B-Instruct
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Metric : first
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Shot : 0shot
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average accuracy 56.453145004749516
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accuracy for STEM 56.73352435530086
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accuracy for Language 58.460559796437664
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accuracy for Social science 54.20641803989592
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accuracy for Others 52.554569441112974
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accuracy for Humanities 60.659840728100114
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```
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#### First token match using vLLM
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Based on 0-shot exact first token match using vLLM,
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```
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Model Accuracy shot category
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0 Malaysian-Llama-3.2-3B-Instruct 51.944331 0 STEM
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1 Malaysian-Llama-3.2-3B-Instruct 50.795165 0 Language
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2 Malaysian-Llama-3.2-3B-Instruct 52.732003 0 Social science
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3 Malaysian-Llama-3.2-3B-Instruct 52.026865 0 Others
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4 Malaysian-Llama-3.2-3B-Instruct 54.539249 0 Humanities
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Model : Malaysian-Llama-3.2-3B-Instruct
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Metric : full
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Shot : 0
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average accuracy 52.35617230413414
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accuracy for STEM 51.94433074089234
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accuracy for Language 50.795165394402034
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accuracy for Social science 52.73200346921075
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accuracy for Others 52.02686495562485
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accuracy for Humanities 54.53924914675768
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```
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While the original model,
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```
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Model Accuracy shot category
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0 Llama-3.2-3B-Instruct 50.511666 0 STEM
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1 Llama-3.2-3B-Instruct 49.825064 0 Language
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2 Llama-3.2-3B-Instruct 48.352125 0 Social science
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3 Llama-3.2-3B-Instruct 48.213001 0 Others
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4 Llama-3.2-3B-Instruct 51.990899 0 Humanities
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Model : Llama-3.2-3B-Instruct
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Metric : full
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Shot : 0
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average accuracy 49.58906372609755
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accuracy for STEM 50.51166598444535
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accuracy for Language 49.82506361323155
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accuracy for Social science 48.35212489158716
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accuracy for Others 48.21300071959703
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accuracy for Humanities 51.990898748577926
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```
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## Acknowledgement
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Special thanks to https://www.sns.com.my and Nvidia for 8x H100 node! |