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README.md
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license: llama3
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---
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Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows:
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library_name: transformers
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tags:
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- pytorch
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license: llama3
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language:
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- ko
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pipeline_tag: text-generation
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---
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<p align="left">
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<img src="https://huggingface.co/cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0/resolve/main/ocelot.webp" width="50%"/>
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<p>
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# solar-kor-resume
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> Update @ 2024.06.05: First release of Llama3-Ocelot-8B-instruct-v01
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<!-- Provide a quick summary of what the model is/does. -->
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This model card corresponds to the 10.8B Instruct version of the **Llama-Ko** model.
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The train wad done on A100-80GB
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**Resources and Technical Documentation**:
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* [llama Model](beomi/Llama-3-Open-Ko-8B)
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- [Orca-Math](https://huggingface.co/datasets/kuotient/orca-math-korean-dpo-pairs)
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- [ko_Ultrafeedback_binarized](maywell/ko_Ultrafeedback_binarized)
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**Citation**
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**Model Developers**: frcp, nebchi, pepperonipizza97
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## Model Information
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It is an LLM model capable of generating Korean text, trained on a pre-trained base model with high-quality Korean SFT dataset and DPO dataset.
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#### *Inputs and outputs*
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- **Input:** Text string, such as a question, a prompt, or a document to be summarized.
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- **Output:** Generated Korean-language text in response to the input, such as an answer to a question, or a summary of a document.
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#### Running the model on a single / multi GPU
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0")
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model = AutoModelForCausalLM.from_pretrained("cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0", device_map="auto")
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=4096, streamer=streamer)
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text = 'λνλ―Όκ΅μ μλλ μ΄λμΈκ°μ?'
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messages = [
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{
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"role": "user",
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"content": "{}".format(text)
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}
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]
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipe(
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prompt,
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temperature=0.2,
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add_special_tokens=True
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)
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print(outputs[0]["generated_text"][len(prompt):])
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```
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### results
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```python
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λνλ―Όκ΅μ μλλ μμΈνΉλ³μμ
λλ€.
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μμΈνΉλ³μμλ μ²μλ, κ΅νμμ¬λΉ, λλ²μ λ± λνλ―Όκ΅μ μ£Όμ μ λΆκΈ°κ΄μ΄ μμΉν΄ μμ΅λλ€.
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λν μμΈμλ λνλ―Όκ΅μ κ²½μ , λ¬Έν, κ΅μ‘, κ΅ν΅μ μ€μ¬μ§λ‘μ¨ λνλ―Όκ΅μ μλμ΄μ λν λμμ
λλ€.μ κ° λμμ΄ λμκΈΈ λ°λλλ€. λ κΆκΈν μ μ΄ μμΌμλ©΄ μΈμ λ μ§ λ¬Όμ΄λ³΄μΈμ!
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```
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```bibtex
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@misc {cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0,
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author = { {frcp, nebchi, pepperonipizza97} },
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title = { solar-kor-resume},
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year = 2024,
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url = { https://huggingface.co/cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0 },
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publisher = { Hugging Face }
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}
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```
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Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows:
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