Is it possible to fine-tune gemma4 A4B to generate complex legal principles of c

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I’m currently working on a project to build a <strong>100% local AI chatbot</strong> for a government-related use case

<!-- SC_OFF --><div class="md"><p>Fine-tuning an LLM for Japanese translation of legal documents like birth certificates, relationship certificates, character certificates,

<!-- SC_OFF --><div class="md"><p><strong>TL;DR</strong>: Released en_legal_ner_ind_trf v0.1 - InLegalBERT fine-tuned on ~34,700 silver-annotated chunks from 33k Indian SC

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve built a fairly flexible RAG pipeline that was initially designed to handle any type of document (PDFs, reports, mixed

<!-- SC_OFF --><div class="md"><p>Beginner here, now I have a good GPU and ollama using docker. Pulled the Gemma4 weights and was able to add it to cursor using ngrok. </p>

<!-- SC_OFF --><div class="md"><p>One setback of smaller local models seems to be their reliability in calling tools for the harness they're plugged into.</p> <p>I personally

<!-- SC_OFF --><div class="md"><p>is it possible that a moe model can decide how many billion parameters to activate per token according to the task. eg if qwen 3.6 35b a3b - if a

<table> <tr><td> <a href="https://www.reddit.com/r/ChatGPT/comments/1sf3czb/you_still_need_lawyers/"> <img alt="You still need lawyers!"