---
library_name: transformers
tags:
- From_Scratch
- Custom
- YSNRFD
- SLM
- ysnrfd
- Persian_SLM
datasets:
- ysn-rfd/fibonacci_alpaca_to_sharegpt_gpt_format_convert_new_dataset_release
language:
- fa
---


## Model Details   

**WARNINNGS:** This Model IS **Pre-Trained**, **in the future will be finetuned**.

### Model Description

The First Persian SLM By YSNRFD (YASIN ARYANFARD) and AMIRHOSSEIN MEHRDOOST, This Model support Only Persian text Inputs, In The Future I Want Add Englih Language Support.

- **Developed by:** ysnrfd (yasin aryanfard)
- **Funded by:** ysnrfd (yasin aryanfard) and Amirhossein Mehrdoost (https://huggingface.co/fibonacciai)
- **Shared by:** ysnrfd (yasin aryanfard) and Amirhossein Mehrdoost (https://huggingface.co/fibonacciai)
- **Model type:** SLM
- **Language(s) (NLP):** Persian
- **License:** ysnrfd LICENSE




### Training Data

ysnrfd Sample Persian Text
LINK: https://huggingface.co/datasets/ysn-rfd/fibonacci_alpaca_to_sharegpt_gpt_format_convert_new_dataset_release


#### Training Hyperparameters

- **Training regime:** fp32 mixed precision


## Evaluation

Not Yet

### Testing Data, Factors & Metrics

ysnrfd Sample Persian Text

#### Testing Data

Not Yet


#### Summary

The Fisrt Persian SLM Trained From Scratch


- **Hardware Type:** Nvidia Tesla T4 (1)
- **Hours used:** 1H
- **Cloud Provider:** Google Colab

### Model Architecture and Objective

YSNRFD Architecture

#### Hardware

Nvidia Tesla T4

#### Software

Python Code, From Scratch, Pytorch

### Script For Run Model

```python
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import re


model_path = "./Path_To_Model"
print(f"{model_path}...")

tokenizer = GPT2Tokenizer.from_pretrained(model_path)
model = GPT2LMHeadModel.from_pretrained(model_path)


device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"{device}")
model.to(device)
model.eval()  


try:
    end_token_id = tokenizer.convert_tokens_to_ids("### End")
    if end_token_id == tokenizer.unk_token_id:
        end_token_id = None
except:
    end_token_id = None

print("\n------+------+------+------ model is ready for testing +------+------++------\n")
print("type exit for exit")
print("\nYSNRFD")
print("------+------+------+----------------------+------+------++------\n")


while True:
   
    user_input = input("\nYou: ").strip()
    
    
    if user_input.lower() in ["exit"]:
        print("\n good bye (ysnrfd)")
        break
    
    
    prompt = f"### Human: {user_input}\n### Assistant:"
    
   
    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=512
    ).to(device)
    
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=400,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=end_token_id or tokenizer.eos_token_id,
            repetition_penalty=1.05
        )
    
    
    full_response = tokenizer.decode(outputs[0], skip_special_tokens=False)
    
   
    assistant_response = full_response[len(prompt):].strip()
    
    
    if "### End" in assistant_response:
        assistant_response = assistant_response.split("### End")[0].strip()
    
    
    assistant_response = re.sub(r'^###\s*Assistant:\s*', '', assistant_response)
    
    
    if assistant_response:
        print(f"\nBot: {assistant_response}")
    else:
        print("\nBot: please again say")
```