oopere
/

pruned40-gemma-2-2b

@@ -1,199 +1,75 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: gemma
+metrics:
+- accuracy
+- perplexity
+base_model:
+- google/gemma-2-2b
 ---
+# Model Card for oopere/pruned40-gemma-2-2b
 <!-- Provide a quick summary of what the model is/does. -->
+This model is a pruned version of the Gemma-2b architecture, 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 Gemma-2b using structured pruning
+- **Original Model:** google/gemma-2-2b
+- **Pruning Method:** Structured pruning of MLP layers using importance scores based on absolute maximum weights
+- **Size Reduction:** 11.36% (from 2.2B to 1.95B parameters)
+- **Architecture:** Same as original Gemma but with reduced MLP layer sizes
+- **Language(s):** Same as original model
+- **License:** Gemma
+- **Developed by:** [Pere Martra](https://huggingface.co/oopere)
+### Key Findings
+- Maintains moderate performance on binary classification tasks (BoolQ)
+- Significant but manageable degradation on reasoning tasks (ARC-Easy)
+- Substantial impact on long-range comprehension (LAMBADA)
+- Notable increase in perplexity (from 3.71 to 29.68 on LAMBADA-OpenAI)
+### Limitations
+- Considerable reduction in performance on complex language understanding tasks
+- Significant degradation in long-range dependency handling
+- May not be suitable for applications requiring high accuracy on language completion tasks
+- Best suited for simpler classification tasks
+### 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 original architecture structure during pruning
+### Hardware Requirements
+#### Memory Requirements
+- **Base Model:**
+  - Parameters: ~4.4 GB (FP16)
+  - Total Runtime Memory: ~5.5 GB
+- **Pruned Model (40%):**
+  - Parameters: ~3.9 GB (FP16)
+  - Total Runtime Memory: ~4.9 GB
+- **Memory Reduction:**
+  - Parameter Memory: 11.36%
+  - Total Runtime Memory: ~10.9%
+#### Notes:
+- Memory requirements assume FP16 precision
+- Actual memory usage may vary depending on:
+  - Batch size
+  - Sequence length
+  - Implementation details
+  - Runtime environment
+#### Minimum Requirements
+- GPU Memory: 6GB for base model, 5GB for pruned model
+- CPU Memory: 16GB recommended for both models
+## Acknowledgments
+- Thanks to [Mariusz Kurman](https://huggingface.co/mkurman) for creating [llama-pruning](https://github.com/MedITSolutionsKurman/llama-pruning), a library that implements and extends this pruning methodology.