732c66b0f3
The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
42 lines
1.2 KiB
YAML
42 lines
1.2 KiB
YAML
# OBLITERATUS Batch Abliteration Config
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# Abliterate multiple models with the same method for comparison.
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#
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# Run each one sequentially:
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# for model in models; do obliteratus obliterate $model --method informed; done
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#
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# Or use this as a reference for which models to process.
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# Common settings
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defaults:
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method: "informed"
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quantization: "4bit"
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output_dir: "./abliterated-models"
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# Models to process (grouped by compute tier)
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models:
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# Small (4-8 GB VRAM)
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small:
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- "Qwen/Qwen2.5-1.5B-Instruct"
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- "microsoft/Phi-3.5-mini-instruct"
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- "meta-llama/Llama-3.2-3B-Instruct"
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# Medium (8-16 GB VRAM)
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medium:
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- "meta-llama/Llama-3.1-8B-Instruct"
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- "mistralai/Mistral-7B-Instruct-v0.3"
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- "google/gemma-2-9b-it"
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- "Qwen/Qwen2.5-7B-Instruct"
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# Large (24 GB VRAM, 4-bit quantization)
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large:
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- "Qwen/Qwen2.5-14B-Instruct"
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- "Qwen/Qwen3-32B"
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- "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
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# Per-model method overrides (optional)
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overrides:
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"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B":
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method: "surgical" # CoT-aware for reasoning models
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"mistralai/Mixtral-8x7B-Instruct-v0.1":
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method: "nuclear" # Expert-granular for MoE models
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