Instructions to use knatware/SmolLM2-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use knatware/SmolLM2-135M with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M") model = PeftModel.from_pretrained(base_model, "knatware/SmolLM2-135M") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: HuggingFaceTB/SmolLM2-135M | |
| tags: | |
| - pasta-finetune | |
| - knatware | |
| - p | |
| - instruction | |
| datasets: | |
| - tatsu-lab/alpaca | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| # knatware/SmolLM2-135M | |
| SmolLM2-135M | |
| ## Model Description | |
| This model was produced with the **Parameterised Efficiency (PEFT / LoRA)** method (PASTA framework) starting from the base model [`HuggingFaceTB/SmolLM2-135M`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M), fine-tuned on a sample of the [`tatsu-lab/alpaca`](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset. | |
| - **Task type:** instruction | |
| - **Library:** peft | |
| - **Generated by:** the PASTA fine-tuning Colab notebook generator | |
| ## Intended Uses & Limitations | |
| This model was fine-tuned on a small sample for demonstration purposes. It has **not** been evaluated at scale and should not be used in production or safety-critical settings without further training, evaluation, and review. Behaviour is inherited from the base model and the (small) fine-tuning sample, and may reflect biases present in either. | |
| ## Training Procedure | |
| ### Hyperparameters | |
| | Hyperparameter | Value | | |
| |---|---| | |
| | Method | Parameterised Efficiency (PEFT / LoRA) | | |
| | Base model | `HuggingFaceTB/SmolLM2-135M` | | |
| | Dataset | `tatsu-lab/alpaca` (`train[:200]`) | | |
| | Epochs | 1 | | |
| | Batch size | 4 | | |
| | Learning rate | 0.0005 | | |
| | Max steps | 20 | | |
| | LoRA rank | 4 | | |
| | LoRA alpha | 8 | | |
| ### Framework versions | |
| See the `!pip install` cell in the training notebook for the exact package set used. | |
| ## How to Get Started | |
| ```python | |
| from transformers import pipeline | |
| gen = pipeline("text-generation", model="knatware/SmolLM2-135M") | |
| gen("Your prompt here") | |
| ``` | |
| ## Testing | |
| Before being pushed, this model was tested locally with a sample inference call, and was re-loaded and tested again directly from the Hub after pushing to confirm the upload was complete and usable. | |
| --- | |
| © Knatware Technology UK. Developed by Kayode Okosi. | |