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--- |
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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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| Model | Single turn* | Multi turn* | Overall* | |
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|:------------------------------:|:------------:|:-----------:|:--------:| |
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| gemini-1.5-pro-preview-0215 | 7.90 | 6.26 | 7.08 | |
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| xionic-1-72b-20240404 | 7.23 | 6.28 | 6.76 | |
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| Ocelot-Instruct | 6.79 | **6.71** | 6.75 | |
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| allganize/Llama-3-Alpha-Ko-8B-Instruct | 7.14 | 6.09 | 6.61 | |
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