Qwen3-1.7B
Collection
Collection of pruned models based on Qwen/Qwen3-1.7B
β’
51 items
β’
Updated
π― LINUX-optimized | π¦ Medium Light pruning | β‘ 18% weights pruned
This model is a moderate-lightly pruned version of Qwen/Qwen3-1.7B, specialized for LINUX tasks using activation-aware weight pruning (Wanda-style).
| Category | Original | Pruned | Change |
|---|---|---|---|
| Python | 40.0% | 60.0% | β 20.0% |
| Html | 0.0% | 6.7% | β 6.7% |
| Trivia | 100.0% | 100.0% | β |
| Math | 100.0% | 100.0% | β |
| Reasoning | N/A | N/A | |
| Medical | 93.3% | 93.3% | β |
| Linux | 100.0% | 100.0% β | β |
| Writing | 73.3% | 66.7% | β 6.7% |
Average: 72.4% β 75.2% (+2.9%)
Linux Retention: 100.0% of original performance
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("CompactAI/Qwen3-1.7B-linux-medium-light")
tokenizer = AutoTokenizer.from_pretrained("CompactAI/Qwen3-1.7B-linux-medium-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/Qwen3-1.7B |
| Specialization | Linux |
| Prune Mode | Medium Light |
| Pruning Method | Activation-based weight pruning (Wanda) |
| Weight Reduction | 18% weights pruned |
This model is part of the Qwen3-1.7B pruned model collection. Other variants:
This model inherits the license from the base model Qwen/Qwen3-1.7B.
Generated by ZANNPS [Zeto Automatic Neural Network Pruning System]