- super-json-mode VS LLM-with-RAG
- super-json-mode VS gpt-vector-agent
- super-json-mode VS fastassert
- super-json-mode VS llmcontext
- super-json-mode VS x.infer
- super-json-mode VS llm-client-sdk
- super-json-mode VS awesome-generative-ai
- super-json-mode VS lida
- super-json-mode VS llama-cookbook
- super-json-mode VS chameleon-llm
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🧩 Interfacing with different LLMs-chains, vectorstore databases, and autonomous agents.
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fastassert
Dockerized LLM inference server with constrained output (JSON mode), built on top of vLLM and outlines. Faster, cheaper and without rate limits. Compare the quality and latency to your current LLM API provider.
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x.infer
Discontinued Framework agnostic computer vision inference. Run 1000+ models by changing only one line of code. Supports models from transformers, timm, ultralytics, vllm, ollama and your custom model. [GET https://api.github.com/repos/dnth/x.infer: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]
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awesome-generative-ai
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llama-cookbook
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
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chameleon-llm
Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
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The primary programming language of super-json-mode is Jupyter Notebook.