README.md

---
language:
- ms
- en
- zh
- ta
---

# Malaysian Llama 3.1 70B Instruct

Continue finetuning https://huggingface.co/meta-llama/Llama-3.1-70B-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-llama3.1-70b-malaysian-8k?nw=nwuserhuseinzol05

Source code at https://github.com/mesolitica/malaya/tree/master/session/llama3

## Acknowledgement

Special thanks to https://www.sns.com.my for 8x H100 node!