130 lines
4.1 KiB
Markdown
130 lines
4.1 KiB
Markdown
---
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license: llama3
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language:
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- tr
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pipeline_tag: text-generation
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base_model: meta-llama/Meta-Llama-3-8B
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tags:
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- Turkish
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- turkish
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- Llama
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- Llama3
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---
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<img src="./cosmosLLaMa2_r2.png"/>
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# Cosmos LLaMa Instruct
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This model is a fully fine-tuned version of the "meta-llama/Meta-Llama-3-8B-Instruct" model with a 30GB Turkish dataset.
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The Cosmos LLaMa Instruct is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner. Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
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#### Transformers pipeline
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```python
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import transformers
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import torch
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model_id = "ytu-ce-cosmos/Turkish-Llama-8b-Instruct-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_map="auto",
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)
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messages = [
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{"role": "system", "content": "Sen bir yapay zeka asistanısın. Kullanıcı sana bir görev verecek. Amacın görevi olabildiğince sadık bir şekilde tamamlamak. Görevi yerine getirirken adım adım düşün ve adımlarını gerekçelendir."},
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{"role": "user", "content": "Soru: Bir arabanın deposu 60 litre benzin alabiliyor. Araba her 100 kilometrede 8 litre benzin tüketiyor. Depo tamamen doluyken araba kaç kilometre yol alabilir?"},
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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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messages,
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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"][-1])
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```
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#### Transformers AutoModelForCausalLM
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "Sen bir yapay zeka asistanısın. Kullanıcı sana bir görev verecek. Amacın görevi olabildiğince sadık bir şekilde tamamlamak. Görevi yerine getirirken adım adım düşün ve adımlarını gerekçelendir."},
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{"role": "user", "content": "Soru: Bir arabanın deposu 60 litre benzin alabiliyor. Araba her 100 kilometrede 8 litre benzin tüketiyor. Depo tamamen doluyken araba kaç kilometre yol alabilir?"},
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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input_ids,
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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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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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# Acknowledgments
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- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
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- Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant numbers 1016912023 and
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1018512024
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- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
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### Contact
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COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department <br>
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https://cosmos.yildiz.edu.tr/ <br>
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cosmos@yildiz.edu.tr
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# Citation
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```bibtex
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@inproceedings{kesgin2024optimizing,
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title={Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training},
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author={Kesgin, H Toprak and Yuce, M Kaan and Dogan, Eren and Uzun, M Egemen and Uz, Atahan and {\.I}nce, Elif and Erdem, Yusuf and Shbib, Osama and Zeer, Ahmed and Amasyali, M Fatih},
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booktitle={2024 Innovations in Intelligent Systems and Applications Conference (ASYU)},
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pages={1--6},
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year={2024},
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organization={IEEE}
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}
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```
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---
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license: llama3
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---
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