trulens
llama-gpt
trulens | llama-gpt | |
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14 | 7 | |
1,629 | 10,334 | |
7.9% | 1.5% | |
9.8 | 7.4 | |
4 days ago | 13 days ago | |
Jupyter Notebook | TypeScript | |
MIT License | MIT License |
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trulens
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Why Vector Compression Matters
Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.
- FLaNK AI Weekly 18 March 2024
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First 15 Open Source Advent projects
12. TruLens by TruEra | Github | tutorial
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trulens VS agenta - a user suggested alternative
2 projects | 22 Nov 2023
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How are generative AI companies monitoring their systems in production?
3) Hallucination is probably the biggest problem we solve for. To do evals for hallucination, we typically see our users use a combination of groundedness (does the context support the LLM response) and context relevance (is the retrieved context relevant to the query). There's also a bunch more for the evaluations you mentioned (moderation models, sentiment, usefulness, etc.) and it's pretty easy to add custom evals.
Also - my hot take is that gpt-3.5 is good enough for evals (sometimes better) than gpt-4 if you give the LLM enough instructions on how to do the eval.
website: https://www.trulens.org/
- FLaNK Stack Weekly 28 August 2023
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[P] TruLens-Eval is an open source project for eval & tracking LLM experiments.
The team at TruEra recently released an open source project for evaluation & tracking of LLM applications called TruLens-Eval. We’ve specifically targeted retrieval-augmented QA as a core use case and so far we’ve seen it used for comparing different models and parameters, prompts, vector-db configurations and query planning strategies. I’d love to get your feedback on it.
- [D] Hardest thing about building with LLMs?
- Stop Evaluating LLMs on Vibes
- OSS library for attribution and interpretation methods for deep nets
llama-gpt
- FLaNK Stack Weekly 28 August 2023
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Continue with LocalAI: An alternative to GitHub's Copilot that runs locally
wodner if you can pair with https://github.com/getumbrel/llama-gpt
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Show HN: LlamaGPT – Self-hosted, offline, private AI chatbot, powered by Llama 2
I put up a draft PR to demo how to run it on a GPU: https://github.com/getumbrel/llama-gpt/pull/11
It breaks other things like model downloading, but once I got it to a working state for myself, I figured why not put it up there in case its useful. If I have time, I'll try to rework it a little bit with more parameters and less dockerfile repetition to fit the main project better.
- llama-gpt - A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device
What are some alternatives?
langfuse - 🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
serge - A web interface for chatting with Alpaca through llama.cpp. Fully dockerized, with an easy to use API.
probability - Probabilistic reasoning and statistical analysis in TensorFlow
gpt4all - gpt4all: run open-source LLMs anywhere
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
seamless_communication - Foundational Models for State-of-the-Art Speech and Text Translation
embedchain - Personalizing LLM Responses
llm-mlc - LLM plugin for running models using MLC
machine_learning_basics - Plain python implementations of basic machine learning algorithms
prettymapp - 🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp