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README
License: apache-2.0Pruning
- 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 2026-06-15T20:12:41.231682+00:00.
Global sparsity: 0.500000.
Perplexity
| Dataset | PPL | Tokens | Windows |
|---|---|---|---|
| wikitext2 | 12.7344 | 5648 | 3 |
| c4 | 21.4080 | 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/Qwen3-8B 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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