Downloads · 30 days
9
3% of all-time downloads
MilyaShams/Qwen3-1.7B-Pipe_SparseGPT_2_4_GPTQ_W8A8
Qwen3-1.7B-Pipe_SparseGPT_2_4_GPTQ_W8A8 is a machine learning model from MilyaShams. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model was compressed using the llmcompressor framework.
Downloads · 30 days
9
3% of all-time downloads
All-time downloads
259
Public
Parameters
1.3B
1.4 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors1.4 GB · 99%
How the weights are stored.
I8860M · 68%
From the Hugging Face model README
This model was compressed using the llmcompressor framework.
[SparseGPTModifier(index=None, group=None, start=None, end=None, update=None, initialized_=True, finalized_=True, started_=True, ended_=True, sparsity=0.5, sparsity_profile=None, mask_structure='2:4', owl_m=None, owl_lmbda=None, sequential_update=False, sequential_targets=['Qwen3DecoderLayer'], targets=['Linear'], ignore=[], block_size=128, dampening_frac=0.01, preserve_sparsity_mask=False, offload_hessians=False), GPTQModifier(config_groups=None, targets=['Linear'], ignore=[], scheme='W8A8', kv_cache_scheme=None, weight_observer=None, input_observer=None, output_observer=None, observer=None, bypass_divisibility_checks=False, index=None, group=None, start=None, end=None, update=None, initialized_=True, finalized_=True, started_=True, ended_=True, sequential_targets=None, block_size=128, dampening_frac=0.01, actorder=static, offload_hessians=False)]
Note: This model card was automatically generated. All structural modifiers and parameters used during compression are logged above.