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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`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
| **Training** | 1 epoch CosineSimilarityLoss on CPU; pairs from [`hharsha/agentic-systems-showcase`](https://huggingface.co/datasets/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
```python
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`](https://huggingface.co/hharsha/agentic-github-tagger)
- LoRA adapter (generative tiny): [`hharsha/agentic-rag-lora`](https://huggingface.co/hharsha/agentic-rag-lora)
- Dataset: [`hharsha/agentic-systems-showcase`](https://huggingface.co/datasets/hharsha/agentic-systems-showcase)
- Studio: [https://agentic-systems-studio.com](https://agentic-systems-studio.com)
- GitHub: [https://github.com/hharsha98](https://github.com/hharsha98)
## How it was trained
```text
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
```
|