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."	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?

NOTE: Model Supports Auto-model library now! You can run this code to use (Auto-model/Hugging Face transformers):

from transformers import pipeline

# Pipeline
pipe = pipeline(
    "text-generation",
    model="Bertug1911/BrtGPT-1-Pre-Code",
    trust_remote_code=True,
    top_k=40,              # Good for creativity
    temperature=0.8,       # Good for creativity
    max_new_tokens=128     # Default maximum model output (Maximum 1024)
)

# Messages
messages = [
    {"role": "user", "content": "What is the capital of France?"},
]

# Take out
output = pipe(messages)

# Only write asistant's (Model output) answer
assistant_response = output[0]["generated_text"][-1]["content"].strip()
# Special token conversions
formatted_out = assistant_response.replace(" ", "").replace("Ġ", " ").replace("Ċ", "\n")

print(formatted_out)

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]"