72 lines
2.1 KiB
Markdown
72 lines
2.1 KiB
Markdown
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---
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license: llama3
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language:
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- tr
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---
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<img src="https://huggingface.co/CerebrumTech/cere-llama-3-8b-tr/resolve/main/cere2.png"
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alt="CEREBRUM LLM" width="420"/>
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# CERE-LLMA-3-8b-TR
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This model is an fine-tuned version of a Llama3 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner.
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## Model Details
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- **Base Model**: LLMA 3 8B based LLM
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- **Tokenizer Extension**: Specifically extended for Turkish
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- **Training Dataset**: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
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- **Training Method**: Initially with DORA, followed by fine-tuning with LORA
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## Benchmark Results
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- **Winogrande_tr**: 56.16
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- **TruthfulQA_tr_v0.2**: 47.46
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- **Mmlu_tr_v0.2**: 46.46
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- **HellaSwag_tr_v0.2**: 48.87
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- **GSM8k_tr_v0.2**: 25.43
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- **Arc_tr_v0.2**: 41.97
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## Usage Examples
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"Cerebrum/cere-llama-3-8b-tr",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3-8b-tr")
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prompt = "Python'da ekrana 'Merhaba Dünya' nasıl yazılır?"
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messages = [
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{"role": "system", "content": "Sen, Cerebrum Tech tarafından üretilen ve verilen talimatları takip ederek en iyi cevabı üretmeye çalışan yardımcı bir yapay zekasın."},
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{"role": "user", "content": prompt}
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]
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text = 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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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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temperature=0.3,
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top_k=50,
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top_p=0.9,
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max_new_tokens=512,
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repetition_penalty=1,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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