Through techniques like Low-Rank Adaptation (LoRA), Quantized Fine-Tuning (QLoRA), and Direct Preference Optimization (DPO), we can efficiently adapt LLMs to meet the demands of various applications. By harnessing the power of fine-tuning, we can unlock the full potential of LLMs, driving in...
Is anybody kind enough to create a simple vanilla example of how to fine tune Llama 2 using Lora adapters such that it to be later used with vLLM for inference. There is a bit of confusion of whether or not to use quantization when loading the model for fine tuning, apparently vLLM do...
Then I thought, I should "just provide the raw text" to the model as the knowledge base and choose the model which was fine-tuned already on the alpaca dataset (so now the model understands the instructions - for that I will use the "nlpcloud/instruct-gpt-j-fp16" model), and then ...
2️⃣评测:作者用MiniGPT4-v2评测,发现即使在像素预测任务上微调桥接模块和LLM的参数,模型重建像素的能力依然不佳,平均绝对误差能达到20.38,恢复的图像一团糊(p2、p3)。 3️⃣怎么学:作者发现在像素预测任务上训练的时候,用LoRA微调的方式更新视觉编码器(CLIP)的权重提升 ...
By clicking on the training workflow, you will see two definitions. One is for fine-tuning the model through Lora (mainly using alpaca-lora,https://github.com/tloen/alpaca-lora), and the other is to merge the trained model with the base model to get the final model. ...
Advanced RAG Patterns: How to improve RAG peformance ref / ref [17 Oct 2023] Data quality: Clean, standardize, deduplicate, segment, annotate, augment, and update data to make it clear, consistent, and context-rich. Embeddings fine-tuning: Fine-tune embeddings to domain specifics, adjust them...
the prompt, especially early on in the training. However, the results from the paper point to the importance of not just fine-tuning but pre-training the model with the<blah>tokens (or using their nomenclature,<pause>tokens); we are only doing LORA fine-tuning. During training, of course...
the prompt, especially early on in the training. However, the results from the paper point to the importance of not just fine-tuning but pre-training the model with the<blah>tokens (or using their nomenclature,<pause>tokens); we are only doing LORA fine-tuning. During training, of course...