git clone https://github.com/lxe/simple-llm-finetuner.git cd simple-llm-finetuner pip install -r requirements.txt Launch it python app.py Openhttp://127.0.0.1:7860/in your browser. Prepare your training data by separating each sample with 2 blank lines. Paste the whole training dataset ...
LoRA and full finetuning are supported as usual. ControlNet is not yet implemented. Certain features such as segmented timestep selection and Compel long prompt weighting are not yet supported. Parameters have been optimised to get the best results, validated through from-scratch training of SD3 ...
This seems to work really nicely. I just need to tune it a little more to redirect stdio to files instead of loosing it (should make any troubleshooting easier) and ensure our standard logging configuration works fine. #97 thcourbon (http://blog.cafeaumiel.com) Posted on 2010-04-05@...
Open-vocabulary meth- ods (e.g., LSeg [22], OpenSeg [17]) use pre-trained vision- and-language models [20, 33], but still need annotated sam- ples to fine-tune. Weakly supervised methods [1, 2] are free from mask labels but require image-level class la- be...
Simple AI application fine tune with data locally create image using podman and deploy on microshift - praveenkumar/simple-ai-app
Source code for paper "As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss" - MaoXinn/BNF
python run_glue.py --output-path \ --task-name <task_name>\ --model-name <model_name>\ --fine-tune-type <fine_tune_type>\ --bias-terms <bias_terms>\ --gpu-device <gpu_device>\ --learning-rate <learning_rate>\ --epochs <epochs>\ --batch-size <batch_size>\ --optimizer <op...
finetune your florence2 model easy. Contribute to facok/florence2-ft-simple development by creating an account on GitHub.
.github docker docs examples lm_finetuning README.md finetune_on_pregenerated.py pregenerate_training_data.py simple_lm_finetuning.py extract_features.py run_classifier.py run_gpt2.py run_openai_gpt.py run_squad.py run_swag.py run_transfo_xl.py notebooks pytorch_pretrained_bert sam...
Lower-case is fine. When method post It is worth internalizing that during test-execution, it is upon the method keyword that the actual HTTP request is issued. Which suggests that the step should be in the When form, for example: When method post. And steps that follow should logically...