oopere
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pruned40-llama-3.2-3b

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 ---
 library_name: transformers
+license: llama3.2
+metrics:
+- accuracy
+- perplexity
+base_model:
+- meta-llama/Llama-3.2-3B
 ---
+# Model Card for oopere/pruned40-llama-3.2-3b
 <!-- Provide a quick summary of what the model is/does. -->
+This model is a pruned version of the Llama-3.2-3b model, with a parameter reduction of 40% in the MLP Layers.
+The pruning process aims to enhance computational efficiency while maintaining acceptable performance across specific tasks.
+This model is not intended to be used directly, but rather to be fine-tuned for specific tasks where it can achieve equal or superior performance compared to fine-tuning the base model for the same task.
 ## Model Details
+- **Model Type:** Pruned version of LLaMA-3.2 using structured pruning
+- **Original Model:** meta-llama/Llama-3.2-3B
+- **Pruning Method:** Structured pruning of MLP layers using importance scores based on absolute maximum weights
+- **Size Reduction:** 26.2% (from 2.79B to 2.37B parameters)
+- **Architecture:** Same as original LLaMA but with reduced MLP layer sizes
+- **Language(s):** Same as original model
+- **License:** Same as original model
+- **Developed by:** [Pere Martra](https://huggingface.co/oopere)
+### Performance on Standard Benchmarks
+| Benchmark | Original Model | Pruned Model | Relative Change |
+| ---- | ---- | ---- | ---- |
+| ARC-Easy | 65.19% | 47.01% | -27.9% |
+| BoolQ | 64.16% | 42.57% | -33.6% |
+| LAMBADA-OpenAI | 62.20% | 34.54% | -44.5% |
+| LAMBADA-Standard | 53.46% | 28.27% | -47.1% |
+### Key Findings
+- Performance Drop: Pruning to 40% results in significant degradation across all benchmarks, particularly for tasks requiring nuanced reasoning and long-range comprehension.
+- ARC-Easy: Retains moderate accuracy, showing the model is still usable for simpler reasoning tasks despite reduced performance.
+- LAMBADA: Both OpenAI and Standard versions show steep declines, indicating the model struggles with language completion tasks.
+- BoolQ: Performance drops highlight challenges with binary classification tasks.
+### Limitations
+- Severe Impact on Long-Range Dependencies: Performance on tasks like LAMBADA indicates the model struggles with understanding and predicting longer sequences.
+- Lower Usability for High-Accuracy Scenarios: The model's limitations make it less suitable for demanding applications.
+### Implementation Details
+- **Pruning Notebook:** [Detailed implementation and methodology](https://github.com/peremartra/Large-Language-Model-Notebooks-Course/blob/main/6-PRUNING/6_3_pruning_structured_llama3.2-1b_OK.ipynb)
+- **GitHub Repository:** [LLM Course](https://github.com/peremartra/Large-Language-Model-Notebooks-Course)
+### Pruning Method
+- **Technique:** Structured pruning targeting MLP layers
+- **Pruning Ratio:** 40% of neurons removed from MLP layers
+- **Selection Criteria:** Importance scoring based on absolute maximum weights
+- **Architecture Specifics:** Maintained GLU structure during pruning
+### Hardware Requirements
+- Reduced memory footprint compared to original model
+- Can run on hardware with ~30% less memory than original
+## Acknowledgments
+- Thanks to [Mariusz Kurman](https://huggingface.co/mkurman) for creating [llama-pruning](https://github.com/MedITSolutionsKurman/llama-pruning), a library that extends and improve this pruning methodology.