Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Dataset used: codeparrot/xlcost-text-to-code

github: https://github.com/manishzed/LLM-Fine-tune

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "kr-manish/Mistral-7B-autotrain-text-python-vf1"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

#input_text = "Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ;"
input_text ="Program to convert Centimeters to Pixels | Function to convert centimeters to pixels ; Driver Code"
# Tokenize input text
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate output text
output = model.generate(input_ids, max_length=1024, num_return_sequences=1, do_sample=True)

# Decode and print output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

#Program to convert Centimeters to Pixels | Function to convert centimeters to pixels ; Driver Code [/INST] def cmToPixels ( cm ) : NEW_LINE INDENT return ( ( cm * 100 ) / 17 ) NEW_LINE DEDENT cm = 105.25 NEW_LINE print ( round ( cmToPixels ( cm ) , 3 ) ) NEW_LINE