Instructions to use havinash-ai/1b85331b-b626-4786-89a2-f256e4d3f5e3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use havinash-ai/1b85331b-b626-4786-89a2-f256e4d3f5e3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-1.7B-Instruct") model = PeftModel.from_pretrained(base_model, "havinash-ai/1b85331b-b626-4786-89a2-f256e4d3f5e3") - Notebooks
- Google Colab
- Kaggle
1b85331b-b626-4786-89a2-f256e4d3f5e3
This model is a fine-tuned version of unsloth/SmolLM2-1.7B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2561
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Downloads last month
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Model tree for havinash-ai/1b85331b-b626-4786-89a2-f256e4d3f5e3
Base model
HuggingFaceTB/SmolLM2-1.7B Quantized
HuggingFaceTB/SmolLM2-1.7B-Instruct Finetuned
unsloth/SmolLM2-1.7B-Instruct