Update README.md

README.md

@@ -9,202 +9,68 @@ language:
 - ko
 pipeline_tag: translation
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-
+### Pretrained LM
+- [beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) (MIT License)
+
+### Training Dataset
+- [traintogpb/aihub-flores-koen-integrated-sparta-mini-300k](https://huggingface.co/datasets/traintogpb/aihub-flores-koen-integrated-sparta-mini-300k)
+- Can translate in Enlgish-Korean (bi-directional)
+
+### Prompt
+- Template:
+  ```python
+    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'
+  ```
+  Mind that there is a "space (`_`)" at the end of the prompt (unpredictable first token will be popped up).
+  But if you use vLLM, it's okay to remove the final space(`_`).
+
+### Training
+- Trained with QLoRA
+  - PLM: NormalFloat 4-bit
+  - Adapter: BrainFloat 16-bit
+  - Adapted to all the linear layers (around 2.05%)
+- Merge adapters and upscaled in BrainFloat 16-bit precision
+
+### Usage (IMPORTANT)
+- Should remove the EOS token (`<|endoftext|>`, id=46332) at the end of the prompt.
+  ```python
+    # MODEL
+    adapter_name = 'traintogpb/llama-3-enko-translator-8b-qlora-adapter'
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type='nf4',
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_use_double_quant=True
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        max_length=768,
+        quantization_config=bnb_config,
+        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 = "Someday, QWER will be the greatest girl band in the world.""
+    input_prompt = f"Translate this from English to 한국어.\n### English: {text}\n### 한국어:"
+    inputs = tokenizer(input_prompt, max_length=768, 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=768, eos_token_id=tokenizer.eos_token_id)
+  ```
 
 ### Framework versions