Soup
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Learn how to contribute to the Soup CLI project. This guide covers forking, setting up your environment, running tests, linting with ruff, and submitting pull requests for developers.
What Is the Autopilot Feature in Soup CLI? A Complete Guide to Zero-Configuration Fine‑TuningDiscover the Autopilot feature in Soup CLI. This zero-configuration assistant automatically fine-tunes your models by analyzing data, models, and hardware for optimal hyperparameters. No manual tuning needed.
How to Use the Soup CLI Model Registry for Lineage TrackingLearn to use the Soup CLI model registry for robust lineage tracking. Discover how to record training runs, configurations, artifacts, and link derived models with `soup registry` subcommands.
What Is the Purpose of the `cans` Directory in Soup CLI: A Complete Guide to Soup CansDiscover the purpose of the cans directory in Soup CLI. Learn how Soup Cans packages capture, transport, and replay training iterations for reproducible results.
How Soup CLI Tracks Experiments and Metrics: A Deep Dive into the SQLite-Based ExperimentTrackerDiscover how Soup CLI tracks experiments and metrics with its SQLite-based ExperimentTracker. Learn about run metadata, training metrics, and cost estimates stored locally.
How to Specify Multi-Dataset Interleaving in Soup ConfigurationLearn how to specify multi-dataset interleaving in Soup configuration. Easily combine datasets using concat under over or probs strategies for efficient data loading.
What Data Formats Does Soup CLI Support for Fine-Tuning?Soup CLI supports 15+ data formats for fine-tuning including Alpaca, ShareGPT, LLaVA, and DPO. Easily ingest JSONL, CSV, Parquet, and text files for efficient model training.
How to Use the MLX Backend for Apple Silicon Training with SoupUnlock Apple Silicon training with Soup's MLX backend. Easily fine-tune models by configuring your soup.yaml and installing optional dependencies. Start optimizing your ML workflows today.
How to Use the Unsloth Backend for Faster Training in SoupAccelerate Soup training with Unsloth backend. Easily configure your soup.yaml and install unsloth for 4-bit quantization and 2x faster GPU training.
Soup CLI Quantization Methods: Complete Guide to GPTQ, AWQ, HQQ, and MoreExplore Soup CLI's ten quantization methods including GPTQ, AWQ, HQQ, and more. Optimize your models efficiently with this complete guide to supported quantization techniques.
How Soup CLI Handles Automatic Batch Sizing: A Technical Deep DiveLearn how Soup CLI automatically sizes batches using static formulas or GPU memory probes. Discover the best method for your hardware configuration.
Soup CLI LoRA Configurations: Complete Guide to Fine-Tuning with Low-Rank AdaptationExplore Soup CLI LoRA configurations including DoRA, VeRA, and OLoRA. Fine-tune models efficiently with this complete guide to Low-Rank Adaptation.
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