
This model is a fune-tuned version of codet5-large on Typescript instruct-code pairs. 

To run this model, you can use following example:

```
import torch 
device = torch.device('cuda:0') if torch.cuda.is_available() else None
from transformers import AutoTokenizer, T5ForConditionalGeneration

def generate_code(task_description):
    # Prepare the task description
    input_ids = tokenizer.encode(task_description, return_tensors='pt').to(device)

    # Generate the output
    with torch.no_grad():
        output_ids = model.generate(input_ids, max_length=200, temperature=0.7, num_beams=5)

    # Decode the output
    output = tokenizer.decode(output_ids[0], skip_special_tokens=True)

    return output

model = T5ForConditionalGeneration.from_pretrained('mishasadhaker/codet5_large_typescript').to(device)
tokenizer = AutoTokenizer.from_pretrained('mishasadhaker/codet5_large_typescript')

print(generate_code('write function for sum of two numbers and return it'))
```

{"language": ["en"], "tags": ["code"], "datasets": ["bleugreen/typescript-instruct"]}
transformers pytorch t5 text2text-generation code en dataset:bleugreen/typescript-instruct autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #code #en #dataset-bleugreen/typescript-instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


This model is a fune-tuned version of codet5-large on Typescript instruct-code pairs. 

To run this model, you can use following example:



TAGS
#transformers #pytorch #t5 #text2text-generation #code #en #dataset-bleugreen/typescript-instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
