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mesolitica/
Malaysian-Qwen2.5-1.5B-Instruct

Name: Malaysian-Qwen2.5-1.5B-Instruct
Availability: InStock

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qwen2

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Malaysian Qwen 2.5 1.5B Instruct

Continue finetuning https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct on highly curated 1.5B tokens Malaysian instruction dataset.

Improvement

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

Training session

Finetune on mesolitica/Malaysian-SFT to make the model understand Malaysian context.

How we train

LoRA on ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "embed_tokens", "lm_head"].
128 Rank with alpha 256, or alpha of 2.0
Multipacking 8192 context length with proper SDPA causal masking to prevent document contamination and also make sure proper position ids.
Chunk CCE loss for LoRA.
WanDB at https://wandb.ai/huseinzol05/lora-embedding-128-qwen2.5-1.5b-malaysian-8k

Source code at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5

Benchmark

Probability next tokens

Based on 0-shot official MalayMMLU First token accuracy,

                             Model   Accuracy   shot by_letter        category
0  Malaysian-Qwen2.5-1.5B-Instruct  57.347524  0shot      True            STEM
1  Malaysian-Qwen2.5-1.5B-Instruct  61.084606  0shot      True        Language
2  Malaysian-Qwen2.5-1.5B-Instruct  55.854293  0shot      True  Social science
3  Malaysian-Qwen2.5-1.5B-Instruct  54.017750  0shot      True          Others
4  Malaysian-Qwen2.5-1.5B-Instruct  56.336746  0shot      True      Humanities
{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
Model : Malaysian-Qwen2.5-1.5B-Instruct
Metric : first
Shot : 0shot
average accuracy 57.13459711725106
accuracy for STEM 57.34752353663528
accuracy for Language 61.084605597964384
accuracy for Social science 55.85429314830876
accuracy for Others 54.01775005996642
accuracy for Humanities 56.33674630261661

While the original model,

                   Model   Accuracy   shot by_letter        category
0  Qwen2.5-1.5B-Instruct  57.306590  0shot      True            STEM
1  Qwen2.5-1.5B-Instruct  52.862595  0shot      True        Language
2  Qwen2.5-1.5B-Instruct  51.633420  0shot      True  Social science
3  Qwen2.5-1.5B-Instruct  52.554569  0shot      True          Others
4  Qwen2.5-1.5B-Instruct  57.224118  0shot      True      Humanities
{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
Model : Qwen2.5-1.5B-Instruct
Metric : first
Shot : 0shot
average accuracy 53.69842646512204
accuracy for STEM 57.306590257879655
accuracy for Language 52.862595419847324
accuracy for Social science 51.633420063602195
accuracy for Others 52.554569441112974
accuracy for Humanities 57.22411831626849

First token match using vLLM

Based on 0-shot exact first token match using vLLM Guided Decoding,

                             Model   Accuracy  shot        category
0  Malaysian-Qwen2.5-1.5B-Instruct  52.517397     0            STEM
1  Malaysian-Qwen2.5-1.5B-Instruct  54.834606     0        Language
2  Malaysian-Qwen2.5-1.5B-Instruct  50.650477     0  Social science
3  Malaysian-Qwen2.5-1.5B-Instruct  48.380907     0          Others
4  Malaysian-Qwen2.5-1.5B-Instruct  50.693970     0      Humanities
Model : Malaysian-Qwen2.5-1.5B-Instruct
Metric : full
Shot : 0
average accuracy 51.542559781935324
accuracy for STEM 52.51739664347114
accuracy for Language 54.834605597964384
accuracy for Social science 50.65047701647876
accuracy for Others 48.38090669225234
accuracy for Humanities 50.69397042093288