Upload README.md

README.md

@@ -1,3 +1,76 @@
----
-license: llama3.1
----
+---
+language:
+- en
+license: llama3.1
+tags:
+- llama-3.1
+- ncsoft
+- varco
+base_model:
+- meta-llama/Meta-Llama-3.1-8B-Instruct
+---
+
+## Llama-3.1-Varco-8B-Instruct
+
+### About the Model
+
+**Llama-3.1-Varco-8B-Instruct** is a *generative model* based on Meta-Llama-3.1-8B, 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.
+
+- **Developed by:** NC Research, Language Model Team
+- **Languages (NLP):** Korean, English
+- **License:** LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+- **Base model:** [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)
+
+## Uses
+
+### Direct Use
+
+We recommend to use transformers v4.43.0 or later, as advised for Llama-3.1.
+
+```python
+  from transformers import AutoTokenizer, AutoModelForCausalLM
+  import torch
+
+  model = AutoModelForCausalLM.from_pretrained(
+      "NCSOFT/Llama-3.1-Varco-8B-Instruct",
+      torch_dtype=torch.bfloat16,
+      device_map="auto"
+  )
+  tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-3.1-Varco-8B-Instruct")
+
+  messages = [
+      {"role": "system", "content": "You are a helpful assistant Varco. Respond accurately and diligently according to the user's instructions."},
+      {"role": "user", "content": "안녕하세요."}
+  ]
+
+  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+
+  eos_token_id = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>")
+  ]
+  
+  outputs = model.generate(
+      inputs,
+      eos_token_id=eos_token_id,
+      max_length=8192
+  )
+
+  print(tokenizer.decode(outputs[0]))
+```
+
+## Evaluation
+
+### LogicKor
+
+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.
+
+| Model        |  Math  |  Reasoning  |  Writing  |  Coding  |  Understanding  |  Grammer  |  Single turn  |  Multi turn  |  Overall  |
+|--------------|--------|-------------|-----------|----------|-----------------|-----------|---------------|--------------|-----------|
+| [Llama-3.1-Varco-8B-Instruct](https://huggingface.co/NCSOFT/Llama-3.1-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 |
+| [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 |
+| [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 |
+| [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
+| [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 |
+
+