| --- |
| 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)) |
| ``` |
