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Model: oopere/Llama-3.2-1B-pruned-40pct
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+license: llama3.2
+metrics:
+- accuracy
+- perplexity
+base_model:
+- meta-llama/Llama-3.2-1B
+---
+
+# Model Card for oopere/pruned40-llama-1b
+
+<!-- Provide a quick summary of what the model is/does. -->
+This model is a pruned version of the Llama-3.2 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 LLaMA-1.2B using structured pruning
+- **Original Model:** meta-llama/Llama-3.2-1B
+- **Pruning Method:** Structured pruning of MLP layers using importance scores based on absolute maximum weights
+- **Size Reduction:** 26.3% (from 1.24B to 914M 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)
+
+These models are part of the study "[Exploring GLU Expansion Ratios: Structured Pruning in Llama-3.2 Models](https://doi.org/10.31219/osf.io/qgxea)". They explore structured pruning in GLU-based architectures using Llama-3.2 (1B and 3B variants). The pruning experiments target optimal expansion ratios to balance performance, computational efficiency, and environmental sustainability. The models were evaluated across multiple benchmarks, including BoolQ, ARC-Easy, and MUSR, and demonstrate significant efficiency gains while maintaining robust task performance.
+
+
+### Performance on Standard Benchmarks
+
+| Benchmark | Original Model | Pruned Model | Relative Change |
+| ---- | ---- | ---- | ---- |
+| ARC-Easy | 65.19% | 40.19% | -38.7% |
+| BoolQ | 64.16% | 62.11% | -3.2% |
+| LAMBADA-OpenAI | 62.20% | 29.85% | -52.0% |
+| LAMBADA-Standard | 53.46% | 24.78% | -53.6% |
+
+### Key Findings
+- Remarkably maintains strong performance on binary classification tasks (BoolQ)
+- Significant degradation on reasoning tasks (ARC-Easy)
+- Substantial impact on long-range comprehension (LAMBADA)
+- Notable increase in perplexity for language modeling tasks
+
+### 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 GLU structure during pruning
+
+### Hardware Requirements
+- Reduced memory footprint compared to original model
+- Can run on hardware with ~26% 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.
+
+- This model was created following the pruning method described in the paper: The Width Pruning Dichotomy in Llama-3.2
+```
+@misc{martra2025fragileknowledgerobustinstructionfollowing,
+      title={Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2}, 
+      author={Pere Martra},
+      year={2025},
+      eprint={2512.22671},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2512.22671}, 
+}
+```

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.2-1B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 4916,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.48.3"
+}

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special_tokens_map.json

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