yw223/Qwen3-14B-Wanda-unstructured_50 API & Inference Endpoint

Pruning

Method: Wanda
Sparsity type: unstructured_50
Dense artifact label: unstructured_50
Target sparsity: 0.50
Calibration dataset: allenai/c4
Calibration samples: 128
Max sequence length: 2048
Artifact format: standard dense Transformers checkpoint with zeroed weights
Validation

Validated at 2026-06-15T20:18:27.244548+00:00.
Global sparsity: 0.500000.
Perplexity

Dataset	PPL	Tokens	Windows
wikitext2	11.0978	5648	3
c4	19.4669	8188	4
Smoke Generation

Prompt	Status	New Tokens
`Explain Wanda pruning in three concise bullet points.`	pass	64
`Write a Python function that returns the factorial of a non-negative integer.`	pass	64
`Summarize why sparse LLM weights can reduce memory bandwidth.`	pass	64
License and Use

This model is derived from Qwen/Qwen3-14B and is released under the Apache 2.0 license. Use and redistribution must comply with the upstream model license and Hugging Face model terms.
Run Metadata

json
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Qwen3-14B-Wanda-unstructured_50

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