feat: 7-step wizard, experiment tracking, deploy tab, API package refactor

.github/workflows/ci.yml

@@ -96,6 +96,19 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
+      # The ML stack (torch, transformers, bitsandbytes, …) is large.
+      # Reclaim ~25 GB by removing tools the Docker build doesn't need.
+      - name: Free disk space
+        uses: jlumbroso/free-disk-space@main
+        with:
+          tool-cache: false
+          android: true
+          dotnet: true
+          haskell: true
+          large-packages: true
+          docker-images: true
+          swap-storage: true
+
       - name: Set up Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -104,7 +117,7 @@ jobs:
         with:
           context: .
           push: false
-          load: true
+          load: false
           tags: tuneos:ci
           cache-from: type=gha
           cache-to: type=gha,mode=max

README.md

@@ -26,6 +26,14 @@ The application ships in two forms from one codebase:
 
 ---
 
+## What's New
+
+- **7-step fine-tuning wizard** — a guided end-to-end flow from model selection through dataset, technique (LoRA/QLoRA), hyperparameters, live training, and deployment. Opens as a first-class workspace tab.
+- **Experiment tracking** — every training run is recorded in a local SQLite database (`storage/experiments.db`). Run history, hyperparameters, loss curves, and final metrics persist across restarts and are browsable in the Experiments view.
+- **Deploy tab** — after training completes, step 7 provides one-click actions: download the adapter weights, push to Hugging Face Hub, export to GGUF for local inference engines, push to a GitHub repository, and test the model in a built-in chat interface.
+
+---
+
 ## Capabilities
 
 | Domain | Description |
@@ -34,6 +42,8 @@ The application ships in two forms from one codebase:
 | Dataset preparation | Generate, format, and validate instruction and chat datasets prior to training. |
 | Model conversion | Convert weights between Hugging Face, SafeTensors, and GGUF formats for downstream inference engines. |
 | Training analysis | Track loss curves, evaluation metrics, and run history in real time. |
+| Experiment tracking | Persist every fine-tuning run (hyperparameters, loss history, metrics) in a local SQLite database, with comparison and filtering across runs. |
+| Model deployment | Download adapter weights, push to Hugging Face Hub or GitHub, export to GGUF, and test the fine-tuned model via a built-in inference chat. |
 | Model inspection | Explore architecture, tokenization behavior, and configuration of any supported checkpoint. |
 
 ---