Create fine-tuned models with NO-CODE for Ollama & LMStudio!



AI Summary

Summary of Video: Creating a No-Code Fine-Tuned Model with Anything LLM

  1. Introduction
    • Presenter: Timothy Carat, founder of Implex Labs.
    • Topic: No-code method for producing fine-tuned models using Anything LLM.
  2. Feature Availability
    • Current feature available in the dockerized version, not in the desktop app yet (version 1.5.1 or higher).
    • Download desktop client from anythinglm.com.
  3. Fine-Tuning Overview
    • Allows interaction with various models (GPT, Anthropic, local LLMs) to create fine-tuned models.
    • The process involves considerable costs due to GPU pricing for training.
    • Fine-tuning described as difficult for non-technical users.
  4. Process Description
    • Uses a browser-based interface of the dockerized version for demonstration.
    • Setup involves checking current LLM being used and gathering relevant documents.
    • Initial fine-tuning demonstrated with about 14 chat interactions.
    • Fine-tuning is a cloud-based service, allowing users to keep the .ggf file for local use.
  5. Procedure for Fine-Tuning
    • Users provide email and select a base model (currently only LLaMA 38B).
    • The one-time cost for fine-tuning is $250.
    • Process involves bundling data, sending to cloud, receiving a fine-tuned model in under an hour.
  6. Result Evaluation
    • Testing the fine-tuned model showed accurate responses based on prior chats—an improvement over the untuned model.
    • Combination of fine-tuning and retrieval-augmented generation (RAG) provides enhanced model performance.
  7. Model Management
    • Instructions provided for using the fine-tuned model in both OLAMA and LM Studio.
    • Emphasizes ease of integration with applications and system prompts for models.
  8. Conclusion
    • Encourages exploration of fine-tuning for specific needs, such as integrating procedure manuals or specialized information into LLMs.
    • Promotes Anything LLM as an open-source project and hints at future tutorials on local fine-tuning.