malaysian-tinyllama-1.1b-16k-instructions

Full Parameter Finetuning TinyLlama 16384 context length on Malaysian instructions dataset

README at https://github.com/mesolitica/malaya/tree/5.1/session/tiny-llama#instructions-7b-16384-context-length

We use exact Llama2 Instruct chat template.

WandB, https://wandb.ai/mesolitica/fpf-tinyllama-1.1b-hf-instructions-16k-function-call?workspace=user-husein-mesolitica

WandB report, https://wandb.ai/mesolitica/fpf-mallam-5b-instructions-16k/reports/Instruction-finetuning--Vmlldzo2MjE5Njg2

Dataset

Dataset gathered at https://huggingface.co/collections/mesolitica/malaysian-synthetic-dataset-656c2673fe7fe0b1e9e25fe2

Notebook to prepare dataset at https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/combine-malay-no-alignment-multitasks-partial-ultrachat-v2.ipynb

Limitations

This model is a quick demonstration that the base model can be easily fine-tuned to achieve some performance. It does have minimal moderation mechanisms.

how-to

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

def parse_llama_chat(
    messages, 
    function_call = None,
    default_system = 'Anda adalah pembantu AI yang berguna dan mampu jawab segala soalan yang diberikan.'
):
    
    if messages[0]['role'] != 'system':
        system = default_system
        start_index = 0
    else:
        system = messages[0]['content']
        start_index = 1
        
    user_query = messages[-1]['content']

    users, assistants = [], []
    for q in messages[start_index:-1]:
        if q['role'] == 'user':
            users.append(q['content'])
        elif q['role'] == 'assistant':
            assistants.append(q['content'])

    texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n']
    if function_call:
        fs = []
        for f in function_call:
            f = json.dumps(f, indent=4)
            fs.append(f)
        fs = '\n\n'.join(fs)
        texts.append(f'\n[FUNCTIONCALL]\n{fs}\n')
    for u, a in zip(users, assistants):
        texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ')
    texts.append(f'{user_query.strip()} [/INST]')
    prompt = ''.join(texts).strip()
    return prompt

TORCH_DTYPE = 'bfloat16'
nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
)

tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-16k-instructions')
model = AutoModelForCausalLM.from_pretrained(
    'mesolitica/malaysian-tinyllama-1.1b-16k-instructions',
    use_flash_attention_2 = True,
    quantization_config = nf4_config
)

messages = [
    {'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
    {'role': 'user', 'content': 'kwsp tu apa'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
    inputs,
    max_new_tokens=1024,
    top_p=0.95,
    top_k=50,
    temperature=0.9,
    do_sample=True,
    num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))

<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>

kwsp tu apa [/INST] KWSP (Kumpulan Wang Simpanan Pekerja) merupakan sistem persaraan yang disediakan oleh kerajaan Malaysia untuk memberikan simpanan dan kebajikan kepada pekerja dan pekerja yang berumur 55 tahun ke atas. KWSP adalah singkatan bagi "Kumpulan Wang Simpanan Pekerja" dan ia merupakan salah satu dana persaraan yang popular di Malaysia. </s>