76 lines
3.3 KiB
Markdown
76 lines
3.3 KiB
Markdown
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---
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language:
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- en
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- ko
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license: llama3.1
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tags:
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- llama-3.1
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- ncsoft
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- varco
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base_model:
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- meta-llama/Meta-Llama-3.1-8B
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library_name: transformers
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---
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## Llama-VARCO-8B-Instruct
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### About the Model
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**Llama-VARCO-8B-Instruct** is a *generative model* built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.
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- **Developed by:** NC Research, Language Model Team
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- **Languages (NLP):** Korean, English
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- **License:** LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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- **Base model:** [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
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## Uses
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### Direct Use
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We recommend to use transformers v4.43.0 or later, as advised for Llama-3.1.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"NCSOFT/Llama-VARCO-8B-Instruct",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-VARCO-8B-Instruct")
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messages = [
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{"role": "system", "content": "You are a helpful assistant Varco. Respond accurately and diligently according to the user's instructions."},
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{"role": "user", "content": "안녕하세요."}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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eos_token_id = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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inputs,
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eos_token_id=eos_token_id,
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max_length=8192
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)
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print(tokenizer.decode(outputs[0]))
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```
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## Evaluation
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### LogicKor
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We used the [LogicKor](https://github.com/instructkr/LogicKor) code to measure performance. For the judge model, we used the officially recommended gpt-4-1106-preview. The score includes only the 0-shot evaluation provided in the default.
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| Model | Math | Reasoning | Writing | Coding | Understanding | Grammer | Single turn | Multi turn | Overall |
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|--------------|--------|-------------|-----------|----------|-----------------|-----------|---------------|--------------|-----------|
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| [Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct)| 6.71 / 8.57 | 8.86 / 8.29 | 9.86 / 9.71 | 8.86 / 9.29 | 9.29 / 10.0 | 8.57 / 7.86 | 8.69 | 8.95 | 8.82 |
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| [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)| 6.86 / 7.71 | 8.57 / 6.71 | 10.0 / 9.29 | 9.43 / 10.0 | 10.0 / 10.0 | 9.57 / 5.14 | 9.07 | 8.14 | 8.61 |
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| [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)| 4.29 / 4.86 | 6.43 / 6.57 | 6.71 / 5.14 | 6.57 / 6.00 | 4.29 / 4.14 | 6.00 / 4.00 | 5.71 | 5.12 | 5.42 |
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| [Gemma-2-9B-Instruct](https://huggingface.co/google/gemma-2-9b-it)| 6.14 / 5.86 | 9.29 / 9.0 | 9.29 / 8.57 | 9.29 / 9.14 | 8.43 / 8.43 | 7.86 / 4.43 | 8.38 | 7.57 | 7.98
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| [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)| 5.57 / 4.86 | 7.71 / 6.43 | 7.43 / 7.00 | 7.43 / 8.00 | 7.86 / 8.71 | 6.29 / 3.29 | 7.05 | 6.38 | 6.71 |
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