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---
license: apache-2.0
language:
- tr
---
# Morfoz-LLM-8b-v1.0
This model is an extended version of a Llama-3 8B Instruct-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish raw dataset. We utilized Turkish instruction sets created from various open-source for fine-tuning with the LORA method.
## Model Details
- **Base Model**: Meta Llama 3 8B Instruct
- **Tokenizer Extension**: Specifically extended for Turkish
- **Training Dataset**: Cleaned Turkish raw data with custom Turkish instruction sets
- **Training Method**: Fine-tuning with LORA
### LORA Fine-Tuning Configuration
- `lora_alpha`: 16
- `lora_dropout`: 0.05
- `r`: 64
- `target_modules`: "all-linear"
## Usage Examples
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Morfoz-Aigap/Morfoz-LLM-8b-v1.0")
model = AutoModelForCausalLM.from_pretrained("Morfoz-Aigap/Morfoz-LLM-8b-v1.0", torch_dtype=torch.bfloat16, device_map={"": 0},low_cpu_mem_usage=True)
messages = [
{"role": "user", "content": "Kırmızı başlıklı kız adında kısa bir çocuk hikayesi yazabilir misin?"}
]
top_k = 50
top_p = 0.9
temperature = 0.6
def get_formatted_input(messages):
for item in messages:
if item['role'] == "user":
item['content'] = item['content']
break
conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
formatted_input = "\n\n" + conversation
return formatted_input
formatted_input = get_formatted_input(messages)
print(formatted_input)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, do_sample = True, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=256, eos_token_id=terminators, top_p=top_p, temperature=temperature)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
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