
# Code Generation using GPT2-Large
This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation. <br>
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code.

## Model description
This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this [link](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) to know more about the model details.

## Intended Use & Limitations
This model is intended to generate code for the required function based on a small description of the output required.<br>

**Note:** The model is primarily trained with an objective of code generation.

## Usage

You can use this model directly to get the summaries:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load Code Generator LLM and tokenizer from checkpoint
tokenizer = AutoTokenizer.from_pretrained("DeathReaper0965/gpt2_large_code_generator")
model = AutoModelForCausalLM.from_pretrained("DeathReaper0965/gpt2_large_code_generator")
model = model.to("cuda" if torch.cuda.is_available() else "cpu")

inputs = tokenizer("def hello_world():", return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

outputs = model.generate(**inputs,
                         max_new_tokens= 30,
                         num_return_sequences= 1)

print(tokenizer.batch_decode(outputs)[0])

###########OUTPUT###########
def hello_world():
    return "Hello World!"

@app.route("/hello_world")
def hello_world():
    return "Hello World!"
```

> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by [Praneet](https://deathreaper0965.github.io/) | [LinkedIn](http://linkedin.com/in/deathreaper0965) | [GitHub](https://github.com/DeathReaper0965/)
{"license": "mit", "tags": ["text-generation", "code-generation", "gpt2-large"], "datasets": ["codeparrot/codeparrot-clean"], "widget": [{"text": "def add(a,b):", "example_title": "Example 1"}, {"text": "def get_file_size(filename): \"\"\" Return the size of a file. \"\"\"", "example_title": "Example 2"}], "inference": {"parameters": {"max_new_tokens": 10, "num_return_sequences": 1, "do_sample": false}}}
transformers pytorch safetensors gpt2 text-generation code-generation gpt2-large dataset:codeparrot/codeparrot-clean license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code-generation #gpt2-large #dataset-codeparrot/codeparrot-clean #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# Code Generation using GPT2-Large
This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation. <br>
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code.

## Model description
This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this link to know more about the model details.

## Intended Use & Limitations
This model is intended to generate code for the required function based on a small description of the output required.<br>

Note: The model is primarily trained with an objective of code generation.

## Usage

You can use this model directly to get the summaries:



> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by Praneet | LinkedIn | GitHub
# Code Generation using GPT2-Large
This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation. <br>
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code. ## Model description
This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this link to know more about the model details. ## Intended Use & Limitations
This model is intended to generate code for the required function based on a small description of the output required.<br>

Note: The model is primarily trained with an objective of code generation. ## Usage

You can use this model directly to get the summaries:



> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by Praneet | LinkedIn | GitHub
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code-generation #gpt2-large #dataset-codeparrot/codeparrot-clean #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Code Generation using GPT2-Large
This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation. <br>
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code. ## Model description
This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this link to know more about the model details. ## Intended Use & Limitations
This model is intended to generate code for the required function based on a small description of the output required.<br>

Note: The model is primarily trained with an objective of code generation. ## Usage

You can use this model directly to get the summaries:



> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by Praneet | LinkedIn | GitHub
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code-generation #gpt2-large #dataset-codeparrot/codeparrot-clean #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Code Generation using GPT2-Large
This is a GPT2-large model that's further fine-tuned on the Codeparrot dataset with a custom metric focused on code generation. <br>
The Tokenizer is initialized from the GPT2-large and further trained on the same dataset to better align the tokenization for generating code.## Model description
This Model has the same architecture and Parameters as the GPT2-large model. Please refer to this link to know more about the model details.## Intended Use & Limitations
This model is intended to generate code for the required function based on a small description of the output required.<br>

Note: The model is primarily trained with an objective of code generation.## Usage

You can use this model directly to get the summaries:



> Designed and Developed with <span style="color: #e25555;">&hearts;</span> by Praneet | LinkedIn | GitHub