mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-04-25 00:51:20 +00:00

teknium f172f7d4aa Add skills tools and enhance model integration

- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities.
- Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools.
- Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing.
- Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format.
- Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5.
- Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills.
- Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.

2026-01-30 07:39:55 +00:00

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Deduplication Guide

Complete guide to exact, fuzzy, and semantic deduplication.

Exact deduplication

Remove documents with identical content.

from nemo_curator.modules import ExactDuplicates

# Exact deduplication
exact_dedup = ExactDuplicates(
    id_field="id",
    text_field="text",
    hash_method="md5"  # or "sha256"
)

deduped = exact_dedup(dataset)

Performance: ~16× faster on GPU vs CPU

Fuzzy deduplication

Remove near-duplicate documents using MinHash + LSH.

from nemo_curator.modules import FuzzyDuplicates

fuzzy_dedup = FuzzyDuplicates(
    id_field="id",
    text_field="text",
    num_hashes=260,        # MinHash permutations (more = accurate)
    num_buckets=20,        # LSH buckets (more = faster, less recall)
    hash_method="md5",
    jaccard_threshold=0.8  # Similarity threshold
)

deduped = fuzzy_dedup(dataset)

Parameters:

num_hashes: 128-512 (default 260)
num_buckets: 10-50 (default 20)
jaccard_threshold: 0.7-0.9 (default 0.8)

Performance: 16× faster on 8TB dataset (120h → 7.5h)

Semantic deduplication

Remove semantically similar documents using embeddings.

from nemo_curator.modules import SemanticDuplicates

semantic_dedup = SemanticDuplicates(
    id_field="id",
    text_field="text",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    embedding_batch_size=256,
    threshold=0.85,  # Cosine similarity threshold
    device="cuda"
)

deduped = semantic_dedup(dataset)

Models:

all-MiniLM-L6-v2: Fast, 384 dims
all-mpnet-base-v2: Better quality, 768 dims
Custom models supported

Comparison

Method	Speed	Recall	Use Case
Exact	Fastest	100%	Exact matches only
Fuzzy	Fast	~95%	Near-duplicates (recommended)
Semantic	Slow	~90%	Paraphrases, rewrites

Best practices

Start with exact dedup - Remove obvious duplicates
Use fuzzy for large datasets - Best speed/quality trade-off
Semantic for high-value data - Expensive but thorough
GPU acceleration required - 10-16× speedup

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