Qwen2.5-3B-Instruct
Collection
Collection of pruned models based on Qwen/Qwen2.5-3B-Instruct
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55 items
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Updated
π― HTML-optimized | π¦ Extra Light pruning | β‘ 2% weights pruned
This model is a minimally pruned version of Qwen/Qwen2.5-3B-Instruct, specialized for HTML tasks using activation-aware weight pruning (Wanda-style).
| Category | Original | Pruned | Change |
|---|---|---|---|
| Python | 100.0% | 100.0% | β |
| Html | 6.7% | 6.7% β | β |
| Trivia | 100.0% | 100.0% | β |
| Math | 60.0% | 60.0% | β |
| Reasoning | N/A | N/A | |
| Medical | 100.0% | 100.0% | β |
| Linux | 100.0% | 100.0% | β |
| Writing | 73.3% | 73.3% | β |
Average: 77.1% β 77.1% (+0.0%)
Html Retention: 100.0% of original performance
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("CompactAI/Qwen2.5-3B-Instruct-html-extra-light")
tokenizer = AutoTokenizer.from_pretrained("CompactAI/Qwen2.5-3B-Instruct-html-extra-light")
# Example usage
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen2.5-3B-Instruct |
| Specialization | Html |
| Prune Mode | Extra Light |
| Pruning Method | Activation-based weight pruning (Wanda) |
| Weight Reduction | 2% weights pruned |
This model is part of the Qwen2.5-3B-Instruct pruned model collection. Other variants:
This model inherits the license from the base model Qwen/Qwen2.5-3B-Instruct.
Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]