overthinkos/notebook-finetuning
ov-layers/skills/notebook-finetuning/SKILL.md
Unsloth fine-tuning notebook collection provisioned into the workspace volume at deploy time. Data-only layer — no packages, no services, no dependencies. Use when working with notebook-finetuning, Unsloth training notebooks, or unsloth-studio data provisioning.
testing
Updated Apr 24, 2026
$ install --global
skillsauthnpx skillsauth add overthinkos/overthink-plugins notebook-finetuningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- notebook-finetuning
- description:
- |
notebook-finetuning -- Unsloth fine-tuning notebook data layer
Layer Properties
| Property | Value |
|----------|-------|
| Dependencies | (none) |
| Packages | (none) |
| Services | (none) |
| Volumes | workspace -> ~/workspace (from unsloth-studio) |
| Data | data/finetuning -> workspace volume, dest: finetuning |
| Install files | (none) |
How It Works
This is a data layer — it uses the data: field in layer.yml to map a directory of notebooks to a named volume with a subdirectory destination:
info: "Unsloth fine-tuning notebook collection for unsloth-studio"
data:
- src: data/finetuning
volume: workspace
dest: finetuning
At build time, the contents of data/finetuning/ are staged into /data/workspace/finetuning/ inside the image.
At deploy time, when the workspace volume is configured as a bind mount (ov config --bind workspace), ov config copies the staged data into the host-backed volume directory at <workspace>/finetuning/. This seeds the volume with ready-to-use training notebooks.
The dest: finetuning field places the notebooks in a subdirectory rather than the volume root, keeping the workspace organized alongside other content.
Included Data
37 Jupyter notebooks + 1 manifest, organized by training category:
| Category | Notebooks | Models |
|----------|-----------|--------|
| 00-Setup | 00_Unsloth_Setup.ipynb | (general) |
| 01-FastInference | 3 notebooks | Llama, Qwen, Qwen_Think |
| 02-Vision Training | 2 notebooks | Ministral, Pixtral |
| 03-SFT Training | 5 notebooks | Ministral (text + vision), Pixtral, Qwen, Qwen_Think |
| 04-GRPO Training | 5 notebooks | Ministral (text + vision), Pixtral, Qwen, Qwen_Think |
| 05-DPO Training | 2 notebooks | Qwen, Qwen_Think |
| 06-Reward Training | 2 notebooks | Qwen, Qwen_Think |
| 07-RLOO Training | 5 notebooks | Ministral (text + vision), Pixtral, Qwen, Qwen_Think |
| 08-QLoRA | 12 notebooks | Ministral, Qwen_Think (alpha scaling, continual learning, rank comparison, multi-adapter, quantization comparison, target modules) |
Manifest: notebooks.yaml — structured catalog of all notebooks with metadata.
Usage
# image.yml
unsloth-studio:
layers:
- unsloth-studio
- notebook-finetuning
# ... other layers
# Deploy with bind-backed workspace volume
ov config unsloth-studio --bind workspace
# Notebooks are seeded at <workspace>/finetuning/ on first config
ov start unsloth-studio
# Open http://localhost:8888 → navigate to finetuning/
Notebook Compatibility Fixes
The notebooks include several workarounds for upstream library changes:
packing=Truein SFTConfig (19 notebooks) -- Required for TRL 1.0 compatibility. Without it, SFT training fails with the updated TRL APIos.environ["UNSLOTH_ENABLE_FLEX_ATTENTION"] = "0"(16 Ministral/Pixtral notebooks) -- Disables flex_attention to work around a transformers 5.5 bug that crashes these model architecturesmax_memory={0: "14GB"}in model loading (3 Pixtral-12B notebooks) -- Fixes accelerate device_map estimation for Pixtral-12B models that would otherwise OOMmax_prompt_lengthremoved from DPOConfig (2 DPO notebooks) -- Parameter deprecated and removed in TRL 1.0
Used In Images
/ov-images:unsloth-studio/ov-images:jupyter-ml-notebook
Related Skills
/ov:layer-- data field documentation and layer authoring rules/ov:config-- data provisioning duringov configsetup/ov:deploy-- volume backing configuration (bind, named, encrypted)/ov-layers:unsloth-studio-- the Tier 2 meta-layer that owns the workspace volume/ov-layers:notebook-templates-- sibling data layer pattern (starter notebooks for jupyter)/ov-images:unsloth-studio-- the image that includes this layer
When to Use This Skill
Use when the user asks about:
- The notebook-finetuning layer or its contents
- Unsloth fine-tuning notebook templates or training workflows
- How training notebooks get provisioned into the workspace volume
- The
data:field withdest:subdirectory mapping - Which models and training methods are covered by the notebook collection
Related
/ov:test— declarative testing (tests:block,ov image test,ov test)
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