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chore: Regenerate all playbooks

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nvidia/unsloth/README.md
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## Basic idea ## Basic idea
- **Performance-first**: It claims to speed up training (e.g. 2× faster on single GPU, up to 30× in multi-GPU setups) and reduce memory usage compared to standard methods. :contentReference[oaicite:0]{index=0} - **Performance-first**: It claims to speed up training (e.g. 2× faster on single GPU, up to 30× in multi-GPU setups) and reduce memory usage compared to standard methods.
- **Kernel-level optimizations**: Core compute is built with custom kernels (e.g. with Triton) and hand-optimized math to boost throughput and efficiency. :contentReference[oaicite:1]{index=1} - **Kernel-level optimizations**: Core compute is built with custom kernels (e.g. with Triton) and hand-optimized math to boost throughput and efficiency.
- **Quantization & model formats**: Supports dynamic quantization (4-bit, 16-bit) and GGUF formats to reduce footprint, while aiming to retain accuracy. :contentReference[oaicite:2]{index=2} - **Quantization & model formats**: Supports dynamic quantization (4-bit, 16-bit) and GGUF formats to reduce footprint, while aiming to retain accuracy.
- **Broad model support**: Works with many LLMs (LLaMA, Mistral, Qwen, DeepSeek, etc.) and allows training, fine-tuning, exporting to formats like Ollama, vLLM, GGUF, Hugging Face. :contentReference[oaicite:3]{index=3} - **Broad model support**: Works with many LLMs (LLaMA, Mistral, Qwen, DeepSeek, etc.) and allows training, fine-tuning, exporting to formats like Ollama, vLLM, GGUF, Hugging Face.
- **Simplified interface**: Provides easy-to-use notebooks and tools so users can fine-tune models with minimal boilerplate. :contentReference[oaicite:4]{index=4} - **Simplified interface**: Provides easy-to-use notebooks and tools so users can fine-tune models with minimal boilerplate.
## What you'll accomplish ## What you'll accomplish
@ -41,19 +41,22 @@ parameter-efficient fine-tuning methods like LoRA and QLoRA.
- CUDA 13.0 installed: `nvcc --version` - CUDA 13.0 installed: `nvcc --version`
- Internet access for downloading models and datasets - Internet access for downloading models and datasets
##Ancillary files ## Ancillary files
The python test script can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/test_unsloth.py) The Python test script can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/test_unsloth.py)
## Time & risk ## Time & risk
- **Duration**: 30-60 minutes for initial setup and test run **Duration**: 30-60 minutes for initial setup and test run
- **Risks**:
**Risks**:
- Triton compiler version mismatches may cause compilation errors - Triton compiler version mismatches may cause compilation errors
- CUDA toolkit configuration issues may prevent kernel compilation - CUDA toolkit configuration issues may prevent kernel compilation
- Memory constraints on smaller models require batch size adjustments - Memory constraints on smaller models require batch size adjustments
- **Rollback**: Uninstall packages with `pip uninstall unsloth torch torchvision`
**Rollback**: Uninstall packages with `pip uninstall unsloth torch torchvision`.
## Instructions ## Instructions