deepeval
gpt-llm-trainer | deepeval | |
---|---|---|
4 | 22 | |
3,814 | 1,875 | |
- | 18.2% | |
5.4 | 9.9 | |
about 2 months ago | 2 days ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
gpt-llm-trainer
- FLaNK Stack Weekly 06 Nov 2023
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Show HN: Fine-tune your own Llama 2 to replace GPT-3.5/4
Very nice, thanks!
Check out what Matt Shumer put together as well: https://github.com/mshumer/gpt-llm-trainer.
I have used his trainer for auto distillation of GPT-4 into GPT3.5 fine tunes, but plan to do the same for Llama as well.
Cheers!
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[D] Anyone tried gpt-llm-trainer?
Hey guys, so I stumbled upon this Linkedin post, this guy was showing a jupyter notebook on google colab and was explaining step by step how to train your own model to accomplish very specific tasks, and I believe the base model he was using Llama 2 7B Fine tuning version. This is the github link: https://github.com/mshumer/gpt-llm-trainer
- GPT-LLM-Trainer
deepeval
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Unit Testing LLMs with DeepEval
For the last year I have been working with different LLMs (OpenAI, Claude, Palm, Gemini, etc) and I have been impressed with their performance. With the rapid advancements in AI and the increasing complexity of LLMs, it has become crucial to have a reliable testing framework that can help us maintain the quality of our prompts and ensure the best possible outcomes for our users. Recently, I discovered DeepEval (https://github.com/confident-ai/deepeval), an LLM testing framework that has revolutionized the way we approach prompt quality assurance.
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Show HN: Ragas – the de facto open-source standard for evaluating RAG pipelines
Checkout this instead: https://github.com/confident-ai/deepeval
Also has native ragas implementation but supports all models.
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Show HN: Times faster LLM evaluation with Bayesian optimization
Fair question.
Evaluate refers to the phase after training to check if the training is good.
Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!
So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.
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Implemented 12+ LLM evaluation metrics so you don't have to
A link to a reddit post (with no discussion) which links to this repo
https://github.com/confident-ai/deepeval
- Show HN: I implemented a range of evaluation metrics for LLMs that runs locally
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These 5 Open Source AI Startups are changing the AI Landscape
Star DeepEval on GitHub and contribute to the advancement of LLM evaluation frameworks! 🌟
- FLaNK Stack Weekly 06 Nov 2023
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Why we replaced Pinecone with PGVector 😇
Pinecone, the leading closed-source vector database provider, is known for being fast, scalable, and easy to use. Its ability to allow users to perform blazing-fast vector search makes it a popular choice for large-scale RAG applications. Our initial infrastructure for Confident AI, the world’s first open-source evaluation infrastructure for LLMs, utilized Pinecone to cluster LLM observability log data in production. However, after weeks of experimentation, we made the decision to replace it entirely with pgvector. Pinecone’s simplistic design is deceptive due to several hidden complexities, particularly in integrating with existing data storage solutions. For example, it forces a complicated architecture and its restrictive metadata storage capacity made it troublesome for managing data-intensive workloads.
- Show HN: Unit Testing for LLMs
- Show HN: DeepEval – Unit Testing for LLMs (Open Science)
What are some alternatives?
axolotl - Go ahead and axolotl questions
ragas - Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
OpenPipe - Turn expensive prompts into cheap fine-tuned models
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
Llama-2-Onnx
blog-examples
trieve - All-in-one infrastructure for building search, recommendations, and RAG. Trieve combines search language models with tools for tuning ranking and relevance.
openvino_notebooks - 📚 Jupyter notebook tutorials for OpenVINO™
open_model_zoo - Pre-trained Deep Learning models and demos (high quality and extremely fast)
pezzo - 🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
tailspin - 🌀 A log file highlighter