Instructions to use daniel40/987ba2ea-9db6-4689-bef9-9c0a46a2ddf8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/987ba2ea-9db6-4689-bef9-9c0a46a2ddf8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.5") model = PeftModel.from_pretrained(base_model, "daniel40/987ba2ea-9db6-4689-bef9-9c0a46a2ddf8") - Notebooks
- Google Colab
- Kaggle
987ba2ea-9db6-4689-bef9-9c0a46a2ddf8
This model is a fine-tuned version of lmsys/vicuna-7b-v1.5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5414
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 daniel40/987ba2ea-9db6-4689-bef9-9c0a46a2ddf8
Base model
lmsys/vicuna-7b-v1.5