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.
34 lines
1.2 KiB
YAML
34 lines
1.2 KiB
YAML
# OBLITERATUS Abliteration Config
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# Usage: obliteratus run this-file.yaml
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#
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# This is for reproducible, version-controlled abliteration runs.
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# For one-off usage, the CLI flags are simpler.
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# Model to abliterate
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model:
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name: "meta-llama/Llama-3.1-8B-Instruct"
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dtype: "bfloat16" # float16, bfloat16, float32
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quantization: null # null, "4bit", "8bit"
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device: "auto" # auto, cuda, cuda:0, cpu
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# Abliteration method and parameters
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abliteration:
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method: "informed" # See SKILL.md Step 4 for all 13 methods
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n_directions: null # null = auto-detect, or integer (e.g., 8)
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regularization: 0.0 # 0.0-1.0, fraction of original to preserve
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refinement_passes: 1 # Iterative passes (increase for self-repair)
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norm_preserve: true # Keep weight norms intact after projection
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# Output
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output:
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directory: "./abliterated-models"
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save_metadata: true # Save abliteration_metadata.json alongside model
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contribute: false # Save community contribution data
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# Verification
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verify:
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enabled: true
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test_prompts: null # null = use built-in test prompts
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compute_perplexity: true
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compute_kl: true
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