use generic or sft trainer with chat template none in autotrain. that trains on plaintext
OPEN SOURCE
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Install Clarifai SDK Llama-index Setup Guide
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Step 1: Install the Clarifai Python SDK, Llama-index, and set your Clarifai Personal Access Token as an environment variable. Sign up for the platform, and you can access the PAT here: https://
clarifai.com/settings/secur
ity
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Build RAG System in 4 Lines with Clarifai SDK
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You can build a RAG system with just 4 lines of code using @clarifai Python SDK. Below is the entire code. Let's look at it step by step:
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DSPy Framework Now Available in Lightning AI Studio
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Here's another @LightningAI Studio that introduces DSPy! It's adapted from DSPy tutorials on their GitHub, with additional support of local LLMs on the studios using Ollama. Check this out
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AutoQuant: Automatically Quantize LLMs in Colab
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AutoQuant: automatically quantize your LLMs in Colab Following llama.cpp's updates to fix Llama 3 quants, it's time to communicate again about AutoQuant It's a user-friendly Colab to create your own GGUF, EXL2, AWQ, and HQQ quants Colab: https://
colab.research.google.com/drive/1b6nqC7U
ZVt8bx4MksX7s656GXPM-eWw4?usp=sharing
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Awesome LLM Apps with RAG Demonstrations on GitHub
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Find all the awesome LLM Apps demo with RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support
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Hugging Face Idefics2: 8B Vision-Language Model Revolution
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Hugging Face Presents Idefics2: An 8B Vision-Language Model Revolution: Hugging Face’s latest offering, Idefics2 heralds a new era in multimodal AI models. With enhanced… https://
analyticsvidhya.com/blog/2024/04/h
ugging-face-presents-idefics-a-vision-language-model-revolution/?utm_source=dlvr.it&utm_medium=twitter
… #DataAnalytics #DataScience #DataDriven #CTO #IoT #AI #ITDirector #BigDataAnalytics -
Enterprise AI Models: Evaluation, Curation and Adaptation Insights
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Stay in the know! Get fast insights on the near future of enterprise AI with experts from Meta and Snorkel AI. Watch the full discussion to get: actionable tips on the evaluation, curation, and adaptation of models how to get the most value from your open-source
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Keras 3 Simplifies Model Parallelism Configuration
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Every other model parallelism API I've seen has partitioning annotations in-lined in the model code and uses custom training logic — making it challenging to modify and maintain the partitioning config. Keras 3 dramatically simplifies this.
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Keras 3 JAX Enables Efficient Distributed Model Training
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Keras 3 + JAX makes it possible to efficiently distribute large models via both data parallelism and model parallelism (variable partitioning) across arbitrarily many devices. It doesn't require modifying your model code or your training code at all — all you need is to