Downloads · 30 days
7
11% of all-time downloads
MilyaShams/Qwen3-1.7B-SmoothQuant_0.6_AutoRound_W8A8_ign
Qwen3-1.7B-SmoothQuant_0.6_AutoRound_W8A8_ign 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
7
11% of all-time downloads
All-time downloads
61
Public
Parameters
1.7B
2 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors2 GB · 99%
How the weights are stored.
I81.4B · 82%
From the Hugging Face model README
This model was compressed using the llmcompressor framework.
[SmoothQuantModifier(index=None, group=None, start=None, end=None, update=None, initialized_=True, finalized_=True, started_=True, ended_=True, smoothing_strength=0.6, mappings=[LayerMap(balance_layers=['re:.*q_proj', 're:.*k_proj', 're:.*v_proj'], smooth_layers='re:.*input_layernorm'), LayerMap(balance_layers=['re:.*gate_proj', 're:.*up_proj'], smooth_layers='re:.*post_attention_layernorm')], ignore=[], num_calibration_steps=None, calibration_function=None), AutoRoundModifier(config_groups=None, targets=['Linear'], ignore=['lm_head'], 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=['Qwen3DecoderLayer'], iters=200, enable_torch_compile=True, batch_size=2, lr=None, device_ids='0,1')]
Note: This model card was automatically generated. All structural modifiers and parameters used during compression are logged above.