finetuner
wandb
finetuner | wandb | |
---|---|---|
36 | 16 | |
1,427 | 8,243 | |
1.2% | 1.6% | |
5.5 | 9.9 | |
about 2 months ago | about 15 hours ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
wandb
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A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
Weights & Biases — The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management. Free tier for personal projects only, with 100 GB of storage included.
- Northlight makes Alan Wake 2 shine
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The last sentence of Lowes conveniently missing from OpenAI...
HuggingFace and wandb.ai (both competitors of OpenAI) both also have "do own research"
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Efficient way to tune a network by changing hyperparameters?
Wandb is the best! https://wandb.ai/
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[D] Monitoring production image models
To track stuff I've used wandb.ai in a company in the past, as someone else pointed out. Regarding metrics... This is really specific to your domain, and it is such a broad question. You could count color pixels, the distribution of intensity histograms, etc etc.
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How to use the colab notebook version of Dall-E mini and bypass the traffic limit - A guide
Step 1: The colab notebook uses wandb.ai, so you need to register for a wandb.ai account beforehand if you want to use the colab notebook. After registering you need to go to your homepage and copy the API key and paste/keep it somewhere.
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Roadmap for learning MLOps (for DevOps engineers)
I want to take a look at tools like https://wandb.ai/ and they would integrate into some of the pipelines I'm playing with.
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What's a sequel that got you thinking "the people who made this COMPLETELY missed the point of the first one"?
does current cgi and ai tech can bring back leslie nielsen? might use unreal engine and https://www.resemble.ai/ or https://wandb.ai/?
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What MLOps tools and processes do you use?
I'm currently working for a MLOps company so I'm heavily using their tools (Weights & Biases) but I've used custom C++ for deployment, Pytorch + fastai for quick experimentation, Weights & Biases for experiment tracking, hyper-parameter tuning + model versioning (hence why I went to work for them), custom database + data pipeline, HoloViz for data visualisation (really nice dashboarding tool), Jenkins for CI/CD, I also love Github Actions.
- [D] Best resources or tools to draw nicer table for comparing different models/frameworks performance
What are some alternatives?
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]
tensorboard - TensorFlow's Visualization Toolkit
Jina AI examples - Jina examples and demos to help you get started
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
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.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
jina - ☁️ Build multimodal AI applications with cloud-native stack
guildai - Experiment tracking, ML developer tools
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
pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras
pysot - SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)