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--- |
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license: apache-2.0 |
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tags: |
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- transformers |
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- smollm |
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- pruned-model |
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- instruct |
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- small-llm |
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- text-generation |
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model_creator: HuggingFaceTB |
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base_model: HuggingFaceTB/SmolLM-135M-Instruct |
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model_name: SmolLM-90M-Instruct-Pruned |
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pipeline_tag: text-generation |
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language: |
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- en |
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--- |
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# SmolLM-90M-Instruct-Pruned 🧠💡 |
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A **pruned** version of [`HuggingFaceTB/SmolLM-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct), reduced from **135M** parameters to approximately **90M** for faster inference and reduced memory usage, while maintaining reasonable performance for instruction-style tasks. |
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## 🔧 What’s Inside |
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- Base: `SmolLM-135M-Instruct` |
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- Parameters: **~90M** |
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- Pruning method: Structured pruning (e.g., attention heads, MLP layers) using PyTorch/NVIDIA pruning tools *(customize if needed)*. |
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- Vocabulary, tokenizer, and training objectives remain **identical** to the base model. |
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## 🚀 Intended Use |
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This model is optimized for: |
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- **Low-latency applications** |
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- **Edge deployments** |
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- **Instruction-following tasks** with compact models |
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- Use in environments with **limited VRAM or compute** |
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### Example Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("your-username/SmolLM-90M-Instruct-Pruned") |
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prompt = "Explain quantum computing to a 10-year-old." |
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inputs = tokenizer(prompt, 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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``` |
