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---
license: apache-2.0
tags:
- transformers
- smollm
- pruned-model
- instruct
- small-llm
- text-generation
model_creator: HuggingFaceTB
base_model: HuggingFaceTB/SmolLM-135M-Instruct
model_name: SmolLM-90M-Instruct-Pruned
pipeline_tag: text-generation
language:
- en
---
# SmolLM-90M-Instruct-Pruned 🧠💡
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.
## 🔧 What’s Inside
- Base: `SmolLM-135M-Instruct`
- Parameters: **~90M**
- Pruning method: Structured pruning (e.g., attention heads, MLP layers) using PyTorch/NVIDIA pruning tools *(customize if needed)*.
- Vocabulary, tokenizer, and training objectives remain **identical** to the base model.
## 🚀 Intended Use
This model is optimized for:
- **Low-latency applications**
- **Edge deployments**
- **Instruction-following tasks** with compact models
- Use in environments with **limited VRAM or compute**
### Example Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
model = AutoModelForCausalLM.from_pretrained("your-username/SmolLM-90M-Instruct-Pruned")
prompt = "Explain quantum computing to a 10-year-old."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
``` |