metadata
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- agents
- rag
- embeddings
- llmops
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets:
- hharsha/agentic-systems-showcase
pipeline_tag: feature-extraction
widget:
- source_sentence: hybrid search and reranking for RAG
sentences:
- >-
RetrievalLab shows advanced RAG with hybrid search and cross-encoder
reranking.
- A cooking recipe for pasta carbonara.
- AgentFleet runs multi-agent task DAGs with cost governance.
agentic-systems-minilm
Small sentence embedding model for semantic search over agentic / RAG / LLMOps project docs.
Comparison note: This is an embeddings model (MiniLM, 384-d), not a generative 7B demo and not a Gradio chat Space. It is meant for retrieval and clustering on CPU.
Model details
| Base | sentence-transformers/all-MiniLM-L6-v2 |
| Training | 1 epoch CosineSimilarityLoss on CPU; pairs from hharsha/agentic-systems-showcase plus short synthetic Q/A about AgentFleet, RetrievalLab, AgentOps Studio, Vibespace, Agent OS, CareerAgent, Control Tower, agentgrid |
| Intended use | Semantic search / clustering of short texts about agentic systems, RAG pipelines, MCP tools, and related portfolio docs |
| Not intended for | Open-domain chat, replacing large embedding models on broad corpora, medical/legal advice |
| License | Apache-2.0 (base); training data MIT showcase |
Light domain adaptation only — weights start from MiniLM-L6-v2.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("hharsha/agentic-systems-minilm")
emb = model.encode(["hybrid search RAG with citations", "AgentFleet multi-agent ops"])
print(emb.shape) # (2, 384)
Related
- Tag generator (text2text):
hharsha/agentic-github-tagger - LoRA adapter (generative tiny):
hharsha/agentic-rag-lora - Dataset:
hharsha/agentic-systems-showcase - Studio: https://agentic-systems-studio.com
- GitHub: https://github.com/hharsha98
How it was trained
Base: sentence-transformers/all-MiniLM-L6-v2
Loss: CosineSimilarityLoss
Epochs: 1 (CPU)
Batch size: 8
Data: name/summary pairs from the showcase dataset + synthetic agentic/RAG pairs