---
datasets:
- hdogrukan/test4
pipeline_tag: text-generation
license: mit
language:
- tr
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---
# ENGPT - v0

<!-- Provide a quick summary of what the model is/does. -->

Our model, fine-tuned with Llama 3.1 8B, has been trained using a dataset generated from the EPDK and TEDAŞ regulations within the Turkish Electricity Distribution system.

## Model Details

It can answer questions related to regulations and provide detailed information about specific materials.

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** hdogrukan ,ecokumus
- **Language(s) (NLP):** Turkish
- **Finetuned from model :** Llama 3.1 8B

### Model Sources 

<!-- Provide the basic links for the model. -->

- **Datasets:**
- https://www.tedas.gov.tr/tr/1/sartnameler/RoutePage/63c658e3d27de36b22f9cef6
- https://www.mevzuat.gov.tr/

### Metrics
-https://wandb.ai/hdogrukan/Fine-tune%20llama-3.1-8b-it%20on%20Turkish%20Energy%20Sector%20V0/reports/Engpt-v0--Vmlldzo5MjIzMTIx


## Uses
!pip install --upgrade transformers
```python
import transformers
import torch

model_id = "hdogrukan/Llama-3.1-8B-Instruct-Energy"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)


messages = [
    {"role": "system", "content": "You are helpful asistant."},
    {"role": "user", "content": "TEDAŞ-MLZ/99-032.E şartnamesini hangi kurum yayınlamıştır?"},
]

outputs = pipeline(
    messages,
    max_new_tokens=1024,
    temperature=0.2
)
# For more accuracy try this!
""" 
outputs = pipeline(
    messages,
    max_new_tokens=512,
    #max_length=50,
    num_return_sequences=3,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    temperature=0.5 )
"""
print(outputs[0]["generated_text"][-1])
```

Output : "TEDAŞ-MLZ/99-032.E şartnamesini Türkiye Elektrik Dağıtım A.Ş. yayınlamıştır."