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  ---
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  base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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  library_name: peft
 
 
 
 
 
 
 
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  ---
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  # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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- - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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  - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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  - **Paper [optional]:** [More Information Needed]
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  - **Demo [optional]:** [More Information Needed]
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  base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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  library_name: peft
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+ datasets:
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+ - iamtarun/python_code_instructions_18k_alpaca
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+ language:
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+ - en
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+ tags:
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+ - code
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+ - text-generation-inference
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  ---
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  # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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+ This model is a fine-tuned version of LLaMA 3.1_8B, optimized specifically for Python code generation. Trained on a dataset of Python code examples, it is designed to generate accurate Python code snippets based on textual prompts. It understands Python syntax, structures, and common coding patterns, making it suitable for tasks such as code completion, function generation, and problem-solving in Python.
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+ This model is particularly useful for developers looking for automated assistance in Python coding tasks, providing suggestions or full code blocks to accelerate the development process. Its specialized training allows it to generate well-formed Python code with a higher degree of accuracy compared to a general-purpose language model.
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+ While the model performs well in generating Python code, it may still require validation to ensure the output adheres to the expected behavior in specific contexts. Integration into IDEs or use cases like code autocompletion tools can enhance developer productivity by reducing manual effort and improving coding efficiency.
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+ This model can be a valuable resource for anyone working with Python, from beginners to experienced programmers seeking code automation.
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  ## Model Details
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+ - **Developed by:** [FerdinandC]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [text generation]
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+ - **Language(s) (NLP):** [python, transformers, peft]
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  - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [meta-llama/Llama-3.1-8B-Instruct]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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+ - **Repository:** [https://huggingface.co/FerdinandC/llama-autocomplete-code-finetuned/tree/main]
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  - **Paper [optional]:** [More Information Needed]
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  - **Demo [optional]:** [More Information Needed]
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