| --- |
| 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 |
| ``` |
|
|
