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--- |
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libray_name: transformers |
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pipeline_tag: text-generation |
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license: other |
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license_name: llama3 |
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license_link: LICENSE |
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language: |
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- ko |
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- en |
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tags: |
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- meta |
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- llama |
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- llama-3 |
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- akallama |
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library_name: transformers |
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--- |
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<a href="https://huggingface.co/collections/mirlab/akallama-66338859b09221f3607fdfcd"> |
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<img src="https://github.com/0110tpwls/project/blob/master/3de500aklm.png?raw=true" width="50%"/> |
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</a> |
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# AKALLAMA |
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We introduce AKALLAMA-70B, korean focused opensource 70b large language model. |
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It demonstrate considerable improvement in korean fluence, specially compared to base llama 3 model. |
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To our knowledge, this is one of the first 70b opensource Korean-speaking language models. |
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### Model Description |
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. |
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- **Developed by:** [Yonsei MIRLab](https://mirlab.yonsei.ac.kr/) |
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- **Language(s) (NLP):** Korean, English |
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- **License:** llama3 |
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- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) |
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## How to use |
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This repo provides full model weight files for AkaLlama-70B-v0.1. |
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# Use with transformers |
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See the snippet below for usage with Transformers: |
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``` |
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import transformers |
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import torch |
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model_id = "mirlab/AkaLlama-llama3-70b-v0.1" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device="auto", |
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) |
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system_prompt = """ |
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""" |
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messages = [ |
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{"role": "system", "content": "system_prompt"}, |
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{"role": "user", "content": "๋ค ์ด๋ฆ์ ๋ญ์ผ?"}, |
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] |
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prompt = pipeline.tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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terminators = [ |
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pipeline.tokenizer.eos_token_id, |
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=256, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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## Training Details |
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### Training Procedure |
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We trained AkaLlama using a preference learning alignment algorithm called [Odds Ratio Preference Optimization (ORPO)](https://huggingface.co/papers/2403.07691). |
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Our training pipeline is almost identical to that of [HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1), aside from minor hyperparameter changes. |
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Please check out Huggingface's [alignment handbook](https://github.com/huggingface/alignment-handbook?tab=readme-ov-file) for further details, including the chat template. |
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### Training Data |
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Detailed descriptions regarding training data will be announced later. |
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### Examples |
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WIP |
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## Special Thanks |
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- Data Center of the Department of Artificial Intelligence at Yonsei University for the computation resources |
