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---
libray_name: transformers
pipeline_tag: text-generation
license: other
license_name: llama3
license_link: LICENSE
language:
- ko
- en
tags:
- meta
- llama
- llama-3
- akallama
library_name: transformers
---
<a href="https://huggingface.co/collections/mirlab/akallama-66338859b09221f3607fdfcd">
<img src="https://github.com/0110tpwls/project/blob/master/image_720.png?raw=true" width="40%"/>
</a>
# AKALLAMA
We introduce AKALLAMA-70B, korean focused opensource 70b large language model.
It demonstrate considerable improvement in korean fluence, specially compared to base llama 3 model.
To our knowledge, this is one of the first 70b opensource Korean-speaking language models.
### Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub.
- **Developed by:** [Yonsei MIRLab](https://mirlab.yonsei.ac.kr/)
- **Language(s) (NLP):** Korean, English
- **License:** llama3
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
## How to use
This repo provides full model weight files for AkaLlama-70B-v0.1.
# Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "mirlab/AkaLlama-llama3-70b-v0.1"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
system_prompt = """
"""
messages = [
{"role": "system", "content": "system_prompt"},
{"role": "user", "content": "네 이름은 뭐야?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## Training Details
### Training Procedure
We trained AkaLlama using a preference learning alignment algorithm called [Odds Ratio Preference Optimization (ORPO)](https://huggingface.co/papers/2403.07691).
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.
Please check out Huggingface's [alignment handbook](https://github.com/huggingface/alignment-handbook?tab=readme-ov-file) for further details, including the chat template.
### Training Data
Detailed descriptions regarding training data will be announced later.
### Examples
WIP
## Special Thanks
- Data Center of the Department of Artificial Intelligence at Yonsei University for the computation resources |