README.md

---
datasets:
- iamtarun/python_code_instructions_18k_alpaca
---
# CodeGen-350M-mono-18k-Alpaca-Python
Hugging Face Model
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This repository contains a fine-tuned language model, "CodeGen-350M-mono-18k-Alpaca-Python," which is based on the Salesforce-codegen-350M model and fine-tuned on the "iamtarun/python_code_instructions_18k_alpaca" dataset. This model is designed to assist developers in generating Python code instructions and snippets based on natural language prompts.

Model Details
Model Name: CodeGen-350M-mono-18k-Alpaca-Python
Base Model: Salesforce-codegen-350M
Dataset: iamtarun/python_code_instructions_18k_alpaca
Model Size: 350 million parameters
Usage
You can use this model in various NLP tasks that involve generating Python code from natural language prompts. Below is an example of how to use this model with the Hugging Face Transformers library in Python:

python
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from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/codegen-350M-mono-18k-alpaca-python"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Input text
text = "Create a function that calculates the factorial of a number in Python."

# Tokenize the text
input_ids = tokenizer.encode(text, return_tensors="pt")

# Generate Python code
output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)

# Decode and print the generated code
generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_code)
For more information on using Hugging Face models, refer to the official documentation.

Fine-Tuning Details
The CodeGen-350M-mono-18k-Alpaca-Python model was fine-tuned on the "iamtarun/python_code_instructions_18k_alpaca" dataset using the Hugging Face Transformers library. The fine-tuning process involved adapting the base Salesforce-codegen-350M model to generate Python code instructions specifically for the provided dataset.





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