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--- |
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license: apache-2.0 |
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tags: |
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- pruned |
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- html |
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- optimized |
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- wanda |
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base_model: LiquidAI/LFM2.5-1.2B-Base |
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pipeline_tag: text-generation |
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--- |
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# LFM2.5-1.2B-Base-html-aggressive |
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> **HTML-optimized** | **Aggressive** pruning | **35% weights pruned** |
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This model is a **aggressively pruned** version of [LiquidAI/LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base). |
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> **Pruning Alert:** The benchmarks show virtually NO quality drop! This isn't a bug -- it is a feature. The Wanda pruning algorithm is so effective at identifying unimportant weights that it can remove a large percentage of parameters without affecting performance. Think of it like pruning dead leaves from a tree -- the tree does not miss them because they were not doing anything anyway! |
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## Performance Comparison |
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| Category | Original | Pruned | Change | |
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|----------|----------|--------|--------| |
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| Python | 0.0% | 0.0% | → | |
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| **Html** | 0.0% | 0.0% ⭐ | → | |
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| Trivia | 15.0% | 15.0% | → | |
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| Math | 15.0% | 15.0% | → | |
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| Reasoning | 0.0% | 0.0% | → | |
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| Medical | 20.0% | 20.0% | → | |
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| Linux | 25.0% | 25.0% | → | |
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| Writing | 5.0% | 5.0% | → | |
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**Average**: 10.0% -> 10.0% (+0.0%) |
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## Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("CompactAI/LFM2.5-1.2B-Base-html-aggressive") |
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tokenizer = AutoTokenizer.from_pretrained("CompactAI/LFM2.5-1.2B-Base-html-aggressive") |
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inputs = tokenizer("Your prompt here", return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Technical Details |
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| Property | Value | |
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|----------|-------| |
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| Base Model | [LiquidAI/LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | |
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| Specialization | Html | |
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| Prune Mode | Aggressive | |
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| Weight Reduction | 35% weights pruned | |
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## License |
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This model inherits the license from the base model. |
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