README.md

---
license: cc-by-nc-4.0
datasets:
- nickrosh/Evol-Instruct-Code-80k-v1
- MBZUAI/LaMini-instruction
language:
- en
base_model:
- Bertug1911/BrtGPT-1-Pre
pipeline_tag: text-generation
tags:
- code
---

# BrtGPT-1-Pre-Code

## Model Summary

We're introducing "BratGPT-1-Pre-Code"! Our model was retrained using the "BrtGPT-1-Pre" model, which was already pre-trained, using code data.

Compared to the BrtGPT-1-Pre model, it can write much better code, even with typos.

No change was observed in general/daily chat and simple knowledge-based question-and-answer capabilities.

It may produce some harmful output.

## Difference Between Models

Examples:

| Prompt | BrtGPT-1-Pre |
| :------------: | :------------: |
| "Write me a code that prints "Hello World". | "Here's a code that prints "Hello World" in a list of words:```for i in range(1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5," |
| "Write me a code that generates random number."| Code: |


```
def random(1):
    return random(1)

```

BrtGPT-1-Pre-Code's answers:

1- "Write me a code that prints "Hello World" Code:

```
```python
def print_hello_numbers(numbers):
    if num < num:
        return num
    elif num % num % num % num % num % num % num % num % num % num % num % num % num % num % num % num % num
```

2- "Write me a code that generates random number. Code:

```
#Here is a code that generates random number in python 3:

```python
def generate_random_number(num):
    # Create a new random number between 1 and 1
    random_number = random.randint(num)
    random_number = random.randint(num)
    random_number = random.randint(num)

    # Create a new
```


## How to use?

You can run this code to use:

```
import torch
from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel

def extract_response_between_tokens(text: str) -> str:

    start_token = "<|im_start|>assistant<|im_sep|>"
    end_token = "<|im_end|>"
    try:
        start_idx = text.index(start_token) + len(start_token)
        end_idx = text.index(end_token, start_idx)
        return text[start_idx:end_idx]
    except ValueError:
        # Tokenlar bulunamazsa orijinal metni döndür
        return text

if __name__ == "__main__":
    model_name_or_path = "Bertug1911/BrtGPT-1-Pre"

    tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name_or_path)
    model = GPT2LMHeadModel.from_pretrained(model_name_or_path)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    user_input = input("Enter something to ask model: ")


    messages = [{"role": "user", "content": user_input}]


    formatted_prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )


    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
    generated = inputs["input_ids"]

    # Generate config
    max_new_tokens = 128
    do_sample = True
    top_k = 40
    temperature = 0.8

    im_end_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")

    with torch.no_grad():
        for i in range(max_new_tokens):
            outputs = model(generated)
            logits = outputs.logits[:, -1, :]
            logits = logits / temperature

            if top_k > 0:
                top_k_values, top_k_indices = torch.topk(logits, top_k)
                logits_filtered = torch.full_like(logits, float('-inf'))
                logits_filtered.scatter_(1, top_k_indices, top_k_values)
                logits = logits_filtered

            probs = torch.softmax(logits, dim=-1)

            if do_sample:
                next_token = torch.multinomial(probs, num_samples=1)
            else:
                next_token = torch.argmax(probs, dim=-1, keepdim=True)

            generated = torch.cat([generated, next_token], dim=1)

            if next_token.item() == im_end_token_id:
                break



    output = tokenizer.decode(generated[0], skip_special_tokens=False)

    # Special token conversions
    no_spaces = output.replace(" ", "")
    step2 = no_spaces.replace("Ġ", " ")
    formatted_output = step2.replace("Ċ", "\n")

    if not formatted_output.strip().endswith("<|im_end|>"):
        formatted_output += "<|im_end|>"


    assistant_response = extract_response_between_tokens(formatted_output)
    print("\nModel output:\n", assistant_response)

```
## Evulation

Evulation results is cooming soon!

## Risks and biases

Model may generates:
- Illegal outputs
- Harmfull contents

Use with caution!!

## Contact

"[email protected]" or "[email protected]"