Mike Tanuki

mike-tanuki

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liked a model 20 days ago

Qwen/Qwen2.5-VL-3B-Instruct

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Qwen/Qwen2.5-7B-Instruct

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meta-llama/Llama-3.1-8B-Instruct

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mike-tanuki's activity

liked 5 models 20 days ago

upvoted 5 papers 20 days ago

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Paper • 2502.11089 • Published 22 days ago • 141

EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

Paper • 2502.09509 • Published 25 days ago • 7

Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents

Paper • 2502.11357 • Published 21 days ago • 9

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training

Paper • 2502.11196 • Published 22 days ago • 22

Learning Getting-Up Policies for Real-World Humanoid Robots

Paper • 2502.12152 • Published 21 days ago • 37

liked 2 models 20 days ago

unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit

Text Generation • Updated 24 days ago • 96k • 13

unsloth/Llama-3.1-8B-Instruct

Text Generation • Updated 23 days ago • 13.9k • 1

reacted to as-cle-bert's post with 🔥 20 days ago

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I built an AI agent app in less than 8 hours🤯
And, believe me, this is 𝗻𝗼𝘁 clickbait❌

GitHub 👉 https://github.com/AstraBert/PapersChat
Demo 👉 as-cle-bert/PapersChat

The app is called 𝐏𝐚𝐩𝐞𝐫𝐬𝐂𝐡𝐚𝐭, and it is aimed at 𝗺𝗮𝗸𝗶𝗻𝗴 𝗰𝗵𝗮𝘁𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗽𝗮𝗽𝗲𝗿𝘀 𝗲𝗮𝘀𝗶𝗲𝗿.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐩𝐩 𝐝𝐨𝐞𝐬:

📄 Parses the papers that you upload thanks to LlamaIndex🦙 (either with LlamaParse or with simpler, local methods)
📄 Embeds documents both with a sparse and with a dense encoder to enable hybrid search
📄 Uploads the embeddings to Qdrant
⚙️ Activates an Agent based on mistralai/Mistral-Small-24B-Instruct-2501 that will reply to your prompt
🧠 Retrieves information relevant to your question from the documents
🧠 If no relevant information is found, it searches PubMed and arXiv databases
🧠 Returns a grounded answer to your prompt

𝐇𝐨𝐰 𝐝𝐢𝐝 𝐈 𝐦𝐚𝐧𝐚𝐠𝐞 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐬 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝟖 𝐡𝐨𝐮𝐫𝐬?

Three key points:

- LlamaIndex🦙 provides countless integrations with LLM providers, text embedding models and vectorstore services, and takes care of the internal architecture of the Agent. You just plug it in, and it works!🔌⚡
- Qdrant is a vector database service extremely easy to set up and use: you just need a one-line Docker command😉
- Gradio makes frontend development painless and fast, while still providing modern and responsive interfaces🏗️

And a bonus point:

- Deploying the demo app couldn't be easier if you use Gradio-based Hugging Face Spaces🤗

So, no more excuses: build your own AI agent today and do it fast, (almost) for free and effortlessly🚀

And if you need a starting point, the code for PapersChat is open and fully reproducible on GitHub 👉 https://github.com/AstraBert/PapersChat