Jonathan Avni

Fine-tuning a 3B language model end-to-end on the HF Hub: SFT, DPO, and a vision-language sidetrack

I worked through Hugging Face's Smol Fine-Tuning Language Models course and shipped a preference-aligned small model to the Hub. SmolLM3-3B-Base taken through SFT on 12k summarization examples, then DPO on 12k preference pairs, with DPO continuing to train the same LoRA rather than starting fresh (and the pre-DPO state frozen as the reference policy). A SmolVLM2-2.2B ChartQA adapter sits alongside as a vision-language sidetrack, where LoRA adapts the LLM while the SigLIP vision encoder stays frozen. Four LoRA adapters published, all reproducible from the public code.

An agentic research system with planning, sub-agent delegation, and human-in-the-loop approval

A deep research agent that takes a question, breaks it into a research plan, waits for human approval, then hands research tasks to isolated sub-agents that search the web and synthesize findings. Uses file-based context offloading instead of context stuffing, and runs Gemma 4 locally via Ollama or any cloud LLM.

A minimal AI agent framework: ephemeral containers, living file memory, filesystem IPC

A personal AI agent that runs 24/7 on a VPS, talks through Telegram, manages its own memory, sends emails, and runs scheduled tasks autonomously. Built on the Claude Agent SDK with ephemeral Docker containers, living markdown files for memory, filesystem IPC, and a two-tier skills system (shell scripts + MCP).

An AI that earns and spends money autonomously via x402

An inference service built on top of TinyChat that charges $0.01/query via the x402 payment protocol. Routes complex queries to DeepSeek R1 for ~$0.001, pocketing the difference. Includes complexity classification, a "bar tab" payment mode with stateless HMAC-signed sessions, and wallet integration on Base mainnet.

A 561M-parameter LLM trained from scratch for ~$95

A language model built from scratch: custom BPE tokenizer, GPT architecture with RoPE and Multi-Query Attention, trained on ~38B tokens from FineWeb-EDU, then fine-tuned for conversation. Deployed on Modal serverless GPU with a Next.js frontend.

A full-stack RAG platform for chatting with PDFs, URLs, and podcasts

A RAG platform for chatting with your documents: custom chunking, hybrid retrieval, query classification, multi-document synthesis, and citation validation. Built with Next.js, Supabase, Stripe, and Clerk, deployed on Vercel and Railway.