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@@ -19,7 +19,7 @@ This model is fine-tuned using the Mistral-7B architecture with LoRA to enhance
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  - **Model type:** Language Model (Causal LM)
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  - **Finetuned from model:** Mistral-7B
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- ### Model Sources [optional]
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  - **Repository:** [Hugging Face Model Repository](https://huggingface.co/Hend-10/MistralActia)
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@@ -30,7 +30,7 @@ This model is fine-tuned using the Mistral-7B architecture with LoRA to enhance
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  This model can be used directly for generating code snippets, examples, and explanations in C programming. It is useful for developers, educators, and researchers who need assistance with C code generation.
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- ### Downstream Use [optional]
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  The model can be integrated into applications and tools that assist with coding tasks, such as IDE plugins, coding assistants, and educational platforms for programming.
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@@ -143,11 +143,11 @@ The model demonstrates strong performance in generating coherent and contextuall
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  The model performs well on a range of C programming tasks, though further fine-tuning and evaluation on more diverse datasets could enhance its robustness and applicability.
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- ## Model Examination [optional]
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  The model's interpretability is an ongoing area of research. Examination of model outputs includes analyzing the consistency and relevance of generated code snippets relative to the provided prompts.
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  ## Environmental Impact
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- ## Technical Specifications [optional]
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  ### Model Architecture and Objective
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  - **Libraries:** Datasets, Accelerate, PEFT, TRL
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- ## Citation [optional]
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  **BibTeX:**
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  ```bibtex
 
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  - **Model type:** Language Model (Causal LM)
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  - **Finetuned from model:** Mistral-7B
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+ ### Model Sources
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  - **Repository:** [Hugging Face Model Repository](https://huggingface.co/Hend-10/MistralActia)
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  This model can be used directly for generating code snippets, examples, and explanations in C programming. It is useful for developers, educators, and researchers who need assistance with C code generation.
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+ ### Downstream Use
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  The model can be integrated into applications and tools that assist with coding tasks, such as IDE plugins, coding assistants, and educational platforms for programming.
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  The model performs well on a range of C programming tasks, though further fine-tuning and evaluation on more diverse datasets could enhance its robustness and applicability.
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+ ## Model Examination
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  The model's interpretability is an ongoing area of research. Examination of model outputs includes analyzing the consistency and relevance of generated code snippets relative to the provided prompts.
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  ## Environmental Impact
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+ ## Technical Specifications
151
 
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  ### Model Architecture and Objective
153
 
 
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  - **Libraries:** Datasets, Accelerate, PEFT, TRL
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+ ## Citation
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  **BibTeX:**
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  ```bibtex