tjingrant
/

sparsellm-1b-40p

@@ -1,199 +1,97 @@
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 ---
+language: en
+license: mit
+datasets:
+- fineweb-edu
+tags:
+- llama
+- sparse
+- llm
+- sparse-pretraining
+metrics:
+- perplexity
+arxiv: 2501.12486
 ---
+# tjingrant/sparsellm-1b-40p
+This is a sparse language model based on LLaMA2-1B with 40% sparsity.
 ## Model Details
+- **Model Type:** Sparse Causal Language Model
+- **Base Model:** LLaMA2-1B
+- **Sparsity Configuration:** 40% sparsity
+- **Training Data:** Trained on the Fineweb-Edu dataset
+- **Tokenizer:** Same as the original LLaMA2 model
+- **Perplexity:** 19.93 (measured on Wikitext-103)
+- **Parameter Counts:**
+  - Total Parameters: 1.20B
+  - Total Linear Parameters: 1.14B
+  - Non-zero Linear Parameters: 0.68B
+  - Linear Layer Sparsity: 40.00%
+  - Average Linear Parameters During Training: 0.87B (Average Density: 0.7651)
+## Training Parameters
+- **Training Steps:** 13050
+- **Batch Size:** 8M tokens (4096 × 2048)
+- **Learning Rate:** 0.0003
+- **Total Training Tokens:** 104400000000 (104.4B)
+- **Final Training Loss:** 2.1374 ± 0.0134 (from last 1% of steps)
+- **Pruning Start Step:** 2500
+- **Pruning End Step:** 8875
+- **Matching Dense Model:** [sparsellm-1b-40p-small-dense](https://huggingface.co/tjingrant/sparsellm-1b-40p-small-dense)
+## Performance and Training Details
+Here is the performance and parameter information for all models in this series:
+| Model | Total Params | Linear Params | Avg Linear Params | Non-Zero Linear | Sparsity | Batch Size | LR | Total Tokens | Final Train Loss | Perplexity |
+|-------|--------------|---------------|------------------|----------------|----------|------------|-----|-------------|------------------|------------|
+| [sparsellm-1b-20p](https://huggingface.co/tjingrant/sparsellm-1b-20p) | 1.20B | 1.14B | 1.02B | 0.91B | 20.00% | 8M | 3e-4 | 89.6B | 2.133 ± 0.022 | 19.58 |
+| [sparsellm-1b-40p](https://huggingface.co/tjingrant/sparsellm-1b-40p) | 1.20B | 1.14B | 0.87B | 0.68B | 40.00% | 8M | 3e-4 | 104.4B | 2.137 ± 0.013 | 19.93 |
+| [sparsellm-1b-60p](https://huggingface.co/tjingrant/sparsellm-1b-60p) | 1.20B | 1.14B | 0.69B | 0.46B | 60.00% | 8M | 3e-4 | 131.0B | 2.182 ± 0.017 | 20.80 |
+| [sparsellm-1b-80p](https://huggingface.co/tjingrant/sparsellm-1b-80p) | 1.20B | 1.14B | 0.45B | 0.23B | 80.00% | 8M | 3e-4 | 200.4B | 2.228 ± 0.021 | 25.77 |
+| [sparsellm-1b-20p-small-dense](https://huggingface.co/tjingrant/sparsellm-1b-20p-small-dense) | 1.07B | 1.01B | 1.01B | 1.01B | 0.00% | 8M | 3e-4 | 89.6B | 2.139 ± 0.022 | 19.49 |
+| [sparsellm-1b-40p-small-dense](https://huggingface.co/tjingrant/sparsellm-1b-40p-small-dense) | 0.88B | 0.82B | 0.82B | 0.82B | 0.00% | 8M | 3e-4 | 104.4B | 2.161 ± 0.024 | 21.40 |
+| [sparsellm-1b-60p-small-dense](https://huggingface.co/tjingrant/sparsellm-1b-60p-small-dense) | 0.70B | 0.65B | 0.65B | 0.65B | 0.00% | 8M | 3e-4 | 131.0B | 2.209 ± 0.021 | 22.58 |
+| [sparsellm-1b-80p-small-dense](https://huggingface.co/tjingrant/sparsellm-1b-80p-small-dense) | 0.46B | 0.42B | 0.42B | 0.42B | 0.00% | 8M | 3e-4 | 200.4B | 2.237 ± 0.028 | 24.57 |
+Notes:
+- **Perplexity** is measured on Wikitext-103
+- **Batch Size** is given in tokens (samples × sequence length)
+- **Total Tokens** = Training Steps × Batch Size
+- **Final Train Loss** is computed from the last 1% of training steps (mean ± std)
+- **Avg Linear Params** is the average number of active parameters during training, computed from the pruning schedule
+- Rows 1-4 are sparse models, rows 5-8 are the matching dense models with (approximately) matching average parameter counts over pretraining.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("{model_name}")
+model = AutoModelForCausalLM.from_pretrained("{model_name}")
+inputs = tokenizer("Hello, my name is", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Citation
+If you use this model in your research, please cite our paper:
+**[The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws](https://arxiv.org/abs/2501.12486)**
+```bibtex
+@inproceedings{{
+jin2025the,
+title={{The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws}},
+author={{Tian Jin and Ahmed Imtiaz Humayun and Utku Evci and Suvinay Subramanian and Amir Yazdanbakhsh and Dan Alistarh and Gintare Karolina Dziugaite}},
+booktitle={{The Thirteenth International Conference on Learning Representations}},
+year={{2025}},
+}}
+```