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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."| 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
"bertug2099@gmail.com" or "bertugscpmail@gmail.com" |