grok-1
trulens
grok-1 | trulens | |
---|---|---|
8 | 14 | |
48,188 | 1,646 | |
4.5% | 7.9% | |
5.9 | 9.8 | |
7 days ago | about 18 hours ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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.
grok-1
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Elon Musk's xAI previews Grok-1.5V, its first multimodal model
Anyone know the system requirements? Anyone even able to run it? In their last release "grok 1" the issues are full of people who can't even run it: https://github.com/xai-org/grok-1/issues
- Grok-1 Weights Published
- Grok-1
- FLaNK AI Weekly 18 March 2024
- X.ai's Grok-1 Model Is Officially Open-Source and Larger Than Expected
- Grok-1 (LLM with 314B parameters) is now source
- Elon drops open source Grok onto the stage
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
What are some alternatives?
FLaNK-python-processors - Many processors
langfuse - 🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
rnote - Sketch and take handwritten notes.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
probability - Probabilistic reasoning and statistical analysis in TensorFlow
sqlglot - Python SQL Parser and Transpiler
LIME - Tutorial notebooks on explainable Machine Learning with LIME (Original work: https://arxiv.org/abs/1602.04938)
quarto-cli - Open-source scientific and technical publishing system built on Pandoc.
embedchain - Personalizing LLM Responses
openvino_notebooks - 📚 Jupyter notebook tutorials for OpenVINO™
machine_learning_basics - Plain python implementations of basic machine learning algorithms