Instructions to use ntyazh/llm-course-hw3-tinyllama-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntyazh/llm-course-hw3-tinyllama-qlora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ntyazh/llm-course-hw3-tinyllama-qlora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
The model was created for the task of tweet classification with 3 classes: positive tweet, neutral or negative. It is an adapter for the default TinyLlama/TinyLlama-1.1B-Chat-v1.0 and unfortunately it degrades f1-score on the task from 0.18 to 0.12.
Training Details
Training Data
The model was trained on cardiffnlp/tweet_eval that was created exactly for the given task -- tweet classification.
Training Procedure
The model was trained with trl SFTTrainer for ~260 iterations. LR was cosine with start 5e-5, effective batch size was 32. The rank for the matricies was standard 8, alpha -- 16. AdamW was used as the optimizer.LoRA layers were adapted for v_proj and k_proj layers. Quant type was normal float4, type for the computations was bfloat16
Results
f1 score was degraded from 0.18 to 0.12. I have should to try another hyperparameters and use more epochs for training, I guess