README.md

metadata

library_name: peft
base_model: beomi/open-llama-2-ko-7b
license: cc-by-sa-4.0
datasets:
  - traintogpb/aihub-flores-koen-integrated-sparta-30k
language:
  - en
  - ko
metrics:
  - sacrebleu
  - comet
pipeline_tag: translation

Pretrained LM

• beomi/open-llama-2-ko-7b (MIT License)

Training Dataset

• traintogpb/aihub-flores-koen-integrated-sparta-30k
• Can translate in Enlgish-Korean (bi-directional)

Prompt

• Template:

  prompt = f"Translate this from {src_lang} to {tgt_lang}\n### {src_lang}: {src_text}\n### {tgt_lang}:"
  
  >>> # src_lang can be 'English', '한국어'
  >>> # tgt_lang can be '한국어', 'English'
  

• Issue: The tokenizer of the model tokenizes the prompt below in different way with the prompt above. Make sure to use the prompt proposed above.

  prompt = f"""Translate this from {src_lang} to {tgt_lang}
  ### {src_lang}: {src_text}
  ### {tgt_lang}:"""
  
  >>> # DO NOT USE this prompt.
  

  And mind that there is no "space (_)" at the end of the prompt.

Training

• Trained with QLoRA
  • PLM: NormalFloat 4-bit
  • Adapter: BrainFloat 16-bit
  • Adapted to all the linear layers (around 2.2%)

Usage (IMPORTANT)

• Should remove the EOS token (<|endoftext|>, id=46332) at the end of the prompt.

# MODEL
plm_name = 'beomi/open-llama-2-ko-7b'
adapter_name = 'traintogpb/llama-2-enko-translator-7b-qlora-adapter'
model = LlamaForCausalLM.from_pretrained(
    plm_name, 
    max_length=768,
    quantization_config=bnb_config, # Use the QLoRA config above
    attn_implementation='flash_attention_2',
    torch_dtype=torch.bfloat16
)
model = PeftModel.from_pretrained(
    model, 
    adapter_name, 
    torch_dtype=torch.bfloat16
)

# TOKENIZER
tokenizer = LlamaTokenizer.from_pretrained(plm_name)
tokenizer.pad_token = "</s>"
tokenizer.pad_token_id = 2
tokenizer.eos_token = "<|endoftext|>" # Must be differentiated from the PAD token
tokenizer.eos_token_id = 46332
tokenizer.add_eos_token = True
tokenizer.model_max_length = 768

# INFERENCE
text = "NMIXX is the world-best female idol group, who came back with the new song 'DASH'." 
prompt = f"Translate this from {src_lang} to {tgt_lang}\n### {src_lang}: {src_text}\n### {tgt_lang}:"

inputs = tokenizer(prompt, return_tensors="pt", max_length=max_length, truncation=True)
# REMOVE EOS TOKEN IN THE PROMPT
inputs['input_ids'] = inputs['input_ids'][0][:-1].unsqueeze(dim=0)
inputs['attention_mask'] = inputs['attention_mask'][0][:-1].unsqueeze(dim=0)

outputs = model.generate(**inputs, max_length=max_length, eos_token_id=46332)

input_len = len(inputs['input_ids'].squeeze())
        
translated_text = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True)
print(translated_text)