LLM-Adapters
finetuner
LLM-Adapters | finetuner | |
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2 | 36 | |
963 | 1,435 | |
4.4% | 1.7% | |
7.3 | 5.5 | |
2 months ago | 2 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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LLM-Adapters
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Google DeepMind CEO Says Some Form of AGI Possible in a Few Years
That is not true, you can for example use an additional adapter to optimize, that takes 50$ and a 1 hour. https://github.com/AGI-Edgerunners/LLM-Adapters
- LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of LLMs
finetuner
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How do you think search will change with technology like ChatGPT, Bing’s new AI search engine and the upcoming Google Bard?
And all of that has something to do with finetuners. It basically fine-tunes AI models for specific use cases. With it can create a custom search experience that is tailored to their specific needs. I also wonder how this is going to be integrated into SEO tools soon since those tools are catered to traditional search engines.
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Combining multiple lists into one, meaningfully
Combining multiple lists into one is tough, but it's doable if you have the right approach. Fine-tuning GPT-3 might help, but finding enough examples is tough. You could use existing text data or manually label a set of training examples. A finetuner could be help too. It's a platform-agnostic toolkit that can fine-tune pre-trained models and it's customizable to do lots of tasks.
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speech_recognition not able to convert the full live audio to text. Please help me to fine-tune it.
You can adjust the pause threshold a little longer for pauses between and phrases. You can also use the phrase detection mode, which sets a time limit for the entire phrase instead of ending the transcription prematurely. If your microphone sensitivity is low, you can also try adjusting the energy threshold. If you want, you can use finetuners.
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Questions about fine-tuned results. Should the completion results be identical to fine-tune examples?
It's possible that completion results may be identical to fine-tuned examples, but not guaranteed. Even with the same prompt, slight variations in output are expected due to the nature of probabilistic language models. You can experiment with different settings and parameters, including those with finetuners like these.
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How can I create a dataset to refine Whisper AI from old videos with subtitles?
You can try creating your own dataset. Get some audio data that you want, preprocess it, and then create a custom dataset you can use to fine tune. You could use finetuners like these if you want as well.
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A Guide to Using OpenTelemetry in Jina for Monitoring and Tracing Applications
We derived the dataset by pre-processing the deepfashion dataset using Finetuner. The image label generated by Finetuner is extracted and formatted to produce the text attribute of each product.
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[D] Looking for an open source Downloadable model to run on my local device.
You can either use Hugging Face Transformers as they have a lot of pre-trained models that you can customize. Or Finetuners like this one: which is a toolkit for fine-tuning multiple models.
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
Very recently, a few non-English and multilingual CLIP models have appeared, using various sources of training data. In this article, we’ll evaluate a multilingual CLIP model’s performance in a language other than English, and show how you can improve it even further using Jina AI’s Finetuner.
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Is there a way I can feed the gpt3 model database object like tables? I know we can create fine tune model but not sure about the completion part. Please help!
I think you can convert your data into text and fine-tune the model on it. But that might not be the ideal way to go since you kind of base that on the model. Try transfer learning or finetuning with a finetuner.
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Classification using prompt or fine tuning?
you can try prompt-based classification or fine-tuning with a Finetuner. Prompts work well for simple tasks but fine-tuning may give better results for complex ones. Althouigh it's going to need more resources, but try both and see what works best for you.
What are some alternatives?
TencentPretrain - Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
discus - A data-centric AI package for ML/AI. Get the best high-quality data for the best results. Discord: https://discord.gg/t6ADqBKrdZ
Jina AI examples - Jina examples and demos to help you get started
custom-diffusion - Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
hierarchical-domain-adaptation - Code of NAACL 2022 "Efficient Hierarchical Domain Adaptation for Pretrained Language Models" paper.
jina - ☁️ Build multimodal AI applications with cloud-native stack
AGIEval
Promptify - Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
adapters - A Unified Library for Parameter-Efficient and Modular Transfer Learning
pysot - SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.