ikimyaii

llama-7b-awp-50pct

Model details

Table

Base model	`openlm-research/open_llama_7b`
Method	AWP
Target / achieved sparsity	50% / 50.00%
Calibration	64 batches × 4 × 512 tokens
Weight correction	none (mask search only)
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.

Method

AWP (Liu et al., 2025, arXiv:2506.10205) minimises the activation-weighted reconstruction error ‖(W − Θ) C^{1/2}‖²_F, C = E[xxᵀ], by iterative hard thresholding: Z = Θ + η (W − Θ) C, η = 2/‖C‖_F, then keep the top-k entries per row. It is initialised from the Wanda mask and runs up to 200 iterations (tolerance 1e-4).

Evaluation

Table with columns: Metric, This model, Dense
Metric	This model	Dense
WikiText-2 PPL ↓	10.62	8.64
ARC-Easy (acc_norm)	0.614	0.678
ARC-Challenge (acc_norm)	0.336	0.385
HellaSwag (acc_norm)	0.612	0.694
PIQA (acc_norm)	0.732	0.763
WinoGrande (acc)	0.661

Zero-shot via lm-evaluation-harness; ARC/HellaSwag/PIQA acc_norm, WinoGrande acc. Raw results: results/benchmark_awp_llama_50pct.json.

WikiText-2 PPL of all five methods at 50% (dense 8.64):

Table with columns: Method, WikiText-2 PPL ↓
Method	WikiText-2 PPL ↓
CWS	11.92
SparseGPT	12.70
Wanda	11.43
RIA	11.20
AWP (this model)	10.62

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.

Usage

python

import torchfrom transformers import AutoModelForCausalLM, LlamaTokenizer model_id = "ikimyaii/llama-7b-awp-50pct"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}")

Limitations

These are research checkpoints for studying pruning. They are not instruction-tuned or safety-tuned, and they inherit the base model's biases.
Weights are stored densely, with zeros at the pruned positions. Sparsity alone gives no speedup without a sparse kernel or event-driven hardware.

Sparse-LLMs-on-Loihi: pruning scripts, baselines and raw results for this checkpoint.
CWS: reference implementation of the CWS method these baselines are compared against.
Weight-Sparsity-Using-Real-Loss and EHWS: follow-up work that optimises the real next-token loss instead of layer-wise reconstruction.

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Model Details

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ikimyaii

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Base

openlm-research/open_llama_7b

Fine-tuned

this model

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Model details

Table

Base model	`openlm-research/open_llama_7b`
Method	AWP
Target / achieved sparsity	50% / 50.00%
Calibration	64 batches × 4 × 512 tokens
Weight correction	none (mask search only)
Retraining	none

Method

Evaluation

Table with columns: Metric, This model, Dense
Metric	This model	Dense
WikiText-2 PPL ↓	10.62	8.64
ARC-Easy (acc_norm)	0.614	0.678
ARC-Challenge (acc_norm)	0.336	0.385
HellaSwag (acc_norm)	0.612	0.694
PIQA (acc_norm)	0.732	0.763
WinoGrande (acc)	0.661

Zero-shot via lm-evaluation-harness; ARC/HellaSwag/PIQA acc_norm, WinoGrande acc. Raw results: results/benchmark_awp_llama_50pct.json.

WikiText-2 PPL of all five methods at 50% (dense 8.64):

Table with columns: Method, WikiText-2 PPL ↓
Method	WikiText-2 PPL ↓
CWS	11.92
SparseGPT	12.70
Wanda	11.43
RIA	11.20
AWP (this model)	10.62

Usage

python

import torchfrom transformers import AutoModelForCausalLM, LlamaTokenizer model_id = "ikimyaii/llama-7b-awp-50pct"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}")

Limitations

These are research checkpoints for studying pruning. They are not instruction-tuned or safety-tuned, and they inherit the base model's biases.
Weights are stored densely, with zeros at the pruned positions. Sparsity alone gives no speedup without a sparse kernel or event-driven hardware.

Sparse-LLMs-on-Loihi: pruning scripts, baselines and raw results for this checkpoint.
CWS: reference implementation of the CWS method these baselines are compared against.
Weight-Sparsity-Using-Real-Loss and EHWS: follow-up work that optimises the real next-token loss instead of layer-wise reconstruction.

llama-7b-awp-50pct

README

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README

Model details

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