Instructions to use rayonlabs/tiny-random-LlamaForCausalLM-sql-create-context-61cc1c8a-7201-448c-81e6-9eb79e2d037f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rayonlabs/tiny-random-LlamaForCausalLM-sql-create-context-61cc1c8a-7201-448c-81e6-9eb79e2d037f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "rayonlabs/tiny-random-LlamaForCausalLM-sql-create-context-61cc1c8a-7201-448c-81e6-9eb79e2d037f") - Notebooks
- Google Colab
- Kaggle
c8bdbdea-de18-4492-aa06-2045ef8226f5
This model is a fine-tuned version of trl-internal-testing/tiny-random-LlamaForCausalLM on the None dataset. It achieves the following results on the evaluation set:
- Loss: 10.1782
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
- 4
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support