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Introduction
0/2
101
07:36
101.1 Improving the Performance of Your LLM Beyond Fine Tuning
2. Data Augmentation Techniques to Increase the Quantity and Diversity of Your Data
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201
06:32
101.1 Improving the Performance of Your LLM Beyond Fine Tuning
202
06:32
3. How to use domain adaptation techniques to reduce mismatch and inconsistency
0/5
301
07:16
301.1 Improving the Performance of Your LLM Beyond Fine Tuning
302
08:57
302.1 How to use Hugging Face Spaces for deployment
303
07:16
4. Model Pruning Techniques
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401
03:34
402
02:26
403
05:32
404
02:39
405
04:37
405.1 How to evaluate our LLM model after pruning
5. Model Distillation Techniques
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501
04:15
502
01:55
502.1 How to load and fine-tune our teacher model, which is BERT, for our sentiment analysis task
503
04:13
504
04:40
505
03:53
505.1 How to evaluate our student model after training
6. January 2024 Update To Fine Tuning Methods
0/7
601
41:28
601.1 Fine Tuning Update Lecture Handout
602
17:27
602.1 Chroma Vectorization and Embeddings Handout
603
10:04
604
13:58
604.1 Self-Rewarding Language Models_ A Handout
7. February 2024 Update To Fine Tuning Methods
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701. LORA Tuning Tips and Tricks
17:04
702. Auto Train
06:51
8. March 2024 Update To Fine Tuning Methods
0/2
801. RAFT
07:32
802. GPT Auto Trainer
06:58
9. April 2024 Update Lectures
0/1
901. How Much Data Do You Need For Fine Tuning and Training
17:35
Improving the Performance of Your LLM Beyond Fine Tuning
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