初始化项目，由ModelHub XC社区提供模型
Model: mesolitica/Malaysian-Qwen2.5-7B-Instruct Source: Original Platform
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README.md
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---
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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 Qwen 2.5 7B Instruct
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Continue finetuning https://huggingface.co/Qwen/Qwen2.5-7B-Instruct on highly curated 1.5B tokens Malaysian instruction dataset.
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We provide 2 different revisions,
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1. Rank 128, Alpha 256, [83a0e145c726385502898ab7e016982eae1b684d](https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Instruct/commit/83a0e145c726385502898ab7e016982eae1b684d)
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2. Rank 256, Alpha 512, [5679143eadc2e7deb3bc61ec69ff301b2ba6a4e1](https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Instruct/commit/5679143eadc2e7deb3bc61ec69ff301b2ba6a4e1)
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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. Multipacking 8192 context length with proper SDPA causal masking to prevent document contamination and also make sure proper position ids.
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3. Chunk CCE loss for LoRA.
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### Revision 83a0e145c726385502898ab7e016982eae1b684d
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1. Rank 128, Alpha 256.
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2. WanDB at https://wandb.ai/huseinzol05/lora-embedding-128-qwen2.5-7b-malaysian-8k
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Source code at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5
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### Revision 5679143eadc2e7deb3bc61ec69ff301b2ba6a4e1
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1. Rank 256, Alpha 512.
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2. WanDB at https://wandb.ai/huseinzol05/lora-embedding-256-qwen2.5-7b-malaysian-8k
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Source code at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5
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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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Revision 83a0e145c726385502898ab7e016982eae1b684d,
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```
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Model Accuracy shot by_letter category
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0 Malaysian-Qwen2.5-7B-Instruct 72.042571 0shot True STEM
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1 Malaysian-Qwen2.5-7B-Instruct 70.690204 0shot True Language
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2 Malaysian-Qwen2.5-7B-Instruct 66.536571 0shot True Social science
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3 Malaysian-Qwen2.5-7B-Instruct 67.306308 0shot True Others
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4 Malaysian-Qwen2.5-7B-Instruct 71.808874 0shot True Humanities
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{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
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Model : Malaysian-Qwen2.5-7B-Instruct
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Metric : first
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Shot : 0shot
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average accuracy 69.26031470697559
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accuracy for STEM 72.04257060990585
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accuracy for Language 70.69020356234097
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accuracy for Social science 66.53657126337092
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accuracy for Others 67.30630846725833
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accuracy for Humanities 71.80887372013652
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```
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Revision 5679143eadc2e7deb3bc61ec69ff301b2ba6a4e1,
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```
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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 Qwen2.5-7B-Instruct 70.609906 0shot True STEM
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1 Qwen2.5-7B-Instruct 68.034351 0shot True Language
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2 Qwen2.5-7B-Instruct 63.486557 0shot True Social science
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3 Qwen2.5-7B-Instruct 64.164068 0shot True Others
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4 Qwen2.5-7B-Instruct 69.101251 0shot True Humanities
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{'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443}
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Model : Qwen2.5-7B-Instruct
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Metric : first
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Shot : 0shot
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average accuracy 66.52211621856027
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accuracy for STEM 70.60990585345887
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accuracy for Language 68.03435114503816
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accuracy for Social science 63.486556808326114
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accuracy for Others 64.16406812185176
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accuracy for Humanities 69.10125142207053
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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 Guided Decoding,
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Revision 83a0e145c726385502898ab7e016982eae1b684d,
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```
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Model Accuracy shot category
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0 Malaysian-Qwen2.5-7B-Instruct 70.159640 0 STEM
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1 Malaysian-Qwen2.5-7B-Instruct 66.682570 0 Language
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2 Malaysian-Qwen2.5-7B-Instruct 62.893900 0 Social science
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3 Malaysian-Qwen2.5-7B-Instruct 64.379947 0 Others
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4 Malaysian-Qwen2.5-7B-Instruct 66.780432 0 Humanities
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Model : Malaysian-Qwen2.5-7B-Instruct
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Metric : full
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Shot : 0
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average accuracy 65.57221327386115
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accuracy for STEM 70.15963978714696
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accuracy for Language 66.68256997455471
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accuracy for Social science 62.89389997108991
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accuracy for Others 64.37994722955145
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accuracy for Humanities 66.78043230944255
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```
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Revision 5679143eadc2e7deb3bc61ec69ff301b2ba6a4e1,
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```
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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 Qwen2.5-7B-Instruct 70.978305 0 STEM
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1 Qwen2.5-7B-Instruct 68.177481 0 Language
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2 Qwen2.5-7B-Instruct 64.238219 0 Social science
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3 Qwen2.5-7B-Instruct 64.643799 0 Others
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4 Qwen2.5-7B-Instruct 70.443686 0 Humanities
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Model : Qwen2.5-7B-Instruct
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Metric : full
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Shot : 0
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average accuracy 67.13748812621319
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accuracy for STEM 70.97830536225952
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accuracy for Language 68.17748091603053
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accuracy for Social science 64.23821913847932
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accuracy for Others 64.64379947229551
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accuracy for Humanities 70.44368600682593
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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!
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31
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Normal file
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208
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Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user