Update README.md

README.md

@@ -1,202 +1,138 @@
 ---
-base_model: beomi/Llama-3-Open-Ko-8B
 library_name: peft
+base_model:
+- beomi/Llama-3-Open-Ko-8B
+license: mit
+datasets:
+- traintogpb/aihub-mmt-integrated-prime-base-300k
+language:
+- en
+- ko
+- ja
+- zh
+pipeline_tag: translation
 ---
+### Pretrained LM
+- [beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) (MIT License)
+
+### Training Dataset
+- [traintogpb/aihub-mmt-integrated-prime-base-300k](https://huggingface.co/datasets/traintogpb/aihub-mmt-integrated-prime-base-300k)
+- Can translate in Korean <-> English / Japanese / Chinese (Korean-centered translation)
+
+### Prompt
+- Template:
+  ```python
+    # one of 'src_lang' and 'tgt_lang' should be "한국어"
+    src_lang = "English" # English, 한국어, 日本語, 中文
+    tgt_lang = "한국어" # English, 한국어, 日本語, 中文
+    text = "New era, same empire. T1 is your 2024 Worlds champion!"
+  
+    # task part
+    task_xml_dict = {
+      'head': "<task>",
+      'body': f"Translate the source sentence from {src_lang} to {tgt_lang}.\nBe sure to reflect the guidelines below when translating.",
+      'tail': "</task>"
+    }
+    task = f"{task_xml_dict['head']}\n{task_xml_dict['body']}\n{task_xml_dict['tail']}"
+  
+    # instruction part
+    instruction_xml_dict = {
+      'head': "<instruction>",
+      'body': ["Translate without any condition."],
+      'tail': "</instruction>"
+    }
+    instruction_xml_body = '\n'.join([f'- {body}' for body in instruction_xml_dict['body']])
+    instruction = f"{instruction_xml_dict['head']}\n{instruction_xml_body}\n{instruction_xml_dict['tail']}"
+  
+    # translation part
+    src_xml_dict = {
+      'head': f"<source><{src_lang}>",
+      'body': text.strip(),
+      'tail': f"</{src_lang}></source>"
+    }
+    tgt_xml_dict = {
+      'head': f"<target><{LLAMA_LANG_TABLE[tgt_lang]}>",
+    }
+    src = f"{src_xml_dict['head']}\n{src_xml_dict['body']}\n{src_xml_dict['tail']}"
+    tgt = f"{tgt_xml_dict['head']}\n"
+    translation_xml_dict = {
+      'head': "<translation>",
+      'body': f"{src}\n{tgt}",
+    }
+    translation = f"{translation_xml_dict['head']}\n{translation_xml_dict['body']}"
+  
+    # final prompt
+    prompt = f"{task}\n\n{instruction}\n\n{translation}"
+  ```
+
+- Example Input:
+  ```
+  <task>
+  Translate the source sentence from English to 한국어.
+  Be sure to reflect the guidelines below when translating.
+  </task>
+
+  <instruction>
+  - Translate without any condition.
+  </instruction>
+
+  <translation>
+  <source><English>
+  New era, same empire. T1 is your 2024 Worlds champion!
+  </English></source>
+  <target><한국어>
+  ```
+
+- Expected Output:
+  ```
+  새로운 시대, 여전한 왕조. 티원이 2024 월즈의 챔피언입니다!
+  </한국어></target>
+  </translation>
+  ```
+
+### Training
+- Trained with LoRA adapter
+  - PLM: bfloat16
+  - Adapter: bfloat16
+  - Adapted to all the linear layers (around 2.05%)
+
+### Usage (IMPORTANT)
+- Should remove the EOS token at the end of the prompt.
+  ```python
+    # MODEL
+    model_name = 'beomi/Llama-3-Open-Ko-8B'
+    adapter_name = 'traintogpb/llama-3-mmt-xml-it-sft-adapter'
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        max_length=4000,
+        attn_implementation='flash_attention_2',
+        torch_dtype=torch.bfloat16,
+    )
+    model = PeftModel.from_pretrained(
+        model,
+        adapter_path=adapter_name,
+        torch_dtype=torch.bfloat16,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(adapter_name)
+    tokenizer.pad_token_id = 128002 # eos_token_id and pad_token_id should be different
+
+    text = "New era, same empire. T1 is your 2024 Worlds champion!"
+    input_prompt = "<task> ~ <target><{tgt_lang}>" # prompt with the template above
+    inputs = tokenizer(input_prompt, max_length=2000, truncation=True, return_tensors='pt')
+
+    if inputs['input_ids'][0][-1] == tokenizer.eos_token_id:
+        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=2000, eos_token_id=tokenizer.eos_token_id)
+
+    input_len = len(inputs['input_ids'].squeeze())
+    translation = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True)
+    print(translation)
+  ```
 
-# Model Card for Model ID
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-## Model Details
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-### Model Description
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-## Uses
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Procedure
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-#### Training Hyperparameters
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-#### Summary
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ### Framework versions
 
-- PEFT 0.9.0
+- PEFT 0.8.2