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Model Details
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ikimyaii
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openlm-research/open_llama_7b
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Output Modalities
Supported Functionality
| Base model | openlm-research/open_llama_7b |
| Method | SparseGPT |
| Target / achieved sparsity | 60% / 59.99% |
| Weight correction | OBS (second-order) |
| Retraining | none |
What is pruned: every nn.Linear in the 32 decoder layers (attention q/k/v/o and MLP gate/up/down) plus lm_head, 225 layers in total (see pruning_summary.json). Embeddings and norms stay dense.
SparseGPT (Frantar & Alistarh, 2023, arXiv:2301.00774) scores each weight by its OBS saliency W[i,j]² / [H⁻¹]_jj and applies column-ordered Optimal Brain Surgeon corrections to the remaining weights in blocks of 128 columns. Hessian H = (2/N) XᵀX from calibration activations, damping 0.01.
WikiText-2 PPL of all five methods at 60% (dense 8.64):
| Method | WikiText-2 PPL ↓ |
|---|---|
| CWS | 18.58 |
| SparseGPT (this model) | 20.76 |
| Wanda | 18.11 |
| RIA | 18.54 |
| AWP | 14.69 |
These are the project's results at the time the checkpoints were uploaded (2026-05-11; results/sparsity_sweep_llama.json @ 6e144ae), with dense PPL 8.64. The repository README now reports these models under a 2048-token protocol (dense 6.61); the ordering between methods is similar.
python
import torchfrom transformers import AutoModelForCausalLM, LlamaTokenizer model_id = "ikimyaii/llama-7b-sparsegpt-60pct"tokenizer = LlamaTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") lin = model.model.layers[0].mlp.down_projprint(f"sparsity: {(lin.weight == 0).float().mean():.3f}")import torchfrom transformers import AutoModelForCausalLM, LlamaTokenizer model_id = "ikimyaii/llama-7b-sparsegpt-60pct"tokenizer = LlamaTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") lin = model.model.layers[0].mlp.down_projprint(f"sparsity: {(lin.weight == 0).float().mean():.3f}")