Jeongwon Choi
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README.md
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---
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tags:
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- text-generation
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license: cc-by-nc-sa-4.0
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language:
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- ko
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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pipeline_tag: text-generation
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---
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# **DataVortexM-7B-Instruct-v0.1**
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<img src="https://imgur.com/lpXTyPe.png" alt="DataVortex" style="height: 8em;">
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[cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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## **Model Details**
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### **Base Model**
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### **Trained On**
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### **Instruction format**
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It follows **Alpaca** format.
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## **Model Benchmark**
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### **Ko-LLM-Leaderboard**
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- **Ko-MMLU**: 38.73
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- **Ko-TruthfulQA**: 45.46
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- **Ko-CommonGen V2**: 38.37
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You can use the code below.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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model = AutoModelForCausalLM.from_pretrained("
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tokenizer = AutoTokenizer.from_pretrained("
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messages = [
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{
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]
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decoded
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input_ids=encoded,
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temperature=0.2,
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top_p=0.9,
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repetition_penalty=1.2,
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do_sample=True,
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max_length=4096,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id
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)
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decoded = decoded[0][encoded.shape[1]:decoded[0].shape[-1]]
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decoded_text = tokenizer.decode(decoded, skip_special_tokens=True)
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print(decoded_text)
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```
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<div align="center">
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<a href="https://edentns.com/">
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<img src="
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</a>
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</div>
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---
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tags:
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- text-generation
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license: cc-by-nc-sa-4.0
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language:
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- ko
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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pipeline_tag: text-generation
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datasets:
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- beomi/KoAlpaca-v1.1a
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---
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# **DataVortexM-7B-Instruct-v0.1**
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<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
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## **Model Details**
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### **Base Model**
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[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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### **Trained On**
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- **OS**: Ubuntu 20.04
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- **GPU**: H100 80GB x4
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- **transformers**: v4.36.2
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### **Dataset**
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- [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) - 21k rows
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### **Instruction format**
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It follows **Alpaca** format.
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E.g.
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```python
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text = """\
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### System:
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당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다.
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### User:
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대한민국의 수도는 어디야?
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### Assistant:
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대한민국의 수도는 서울입니다.
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### User:
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서울 인구는 총 몇 명이야?
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"""
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```
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## **Model Benchmark**
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### **Ko-LLM-Leaderboard**
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| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
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| -------------------------------- | --------- | --------- | ------------ | --------- | ------------- | --------------- |
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| **DataVortexM-7B-Instruct-v0.1** | **39.81** | **34.13** | **42.35** | **38.73** | **45.46** | **38.37** |
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## **Implementation Code**
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This model contains the chat_template instruction format.
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You can use the code below.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("DataVortexM-7B-Instruct-v0.1")
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tokenizer = AutoTokenizer.from_pretrained("DataVortexM-7B-Instruct-v0.1")
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messages = [
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{"role": "system", "content": "당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다."},
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{"role": "user", "content": "대한민국의 수도는 어디야?"},
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{"role": "assistant", "content": "대한민국의 수도는 서울입니다."},
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{"role": "user", "content": "서울 인구는 총 몇 명이야?"}
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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## **License**
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The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
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<div align="center">
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<a href="https://edentns.com/">
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<img src="./Logo.png" alt="Logo" style="height: 3em;">
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</a>
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</div>
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