🧩 Model Card: embeddinggemma-300m

• Type: Embedding (Sentence Similarity)
• Think: No
• Base Model: google/embeddinggemma-300m
• Max Chunk Size: 2048
• Default Context Length: NA

▶️ Run with FastFlowLM in PowerShell:

Embedding model requires to use with an LLM (load concurrently) for Server Mode. Embedding model does not work under CLI Mode.

Server Mode

Start with embedding model enabled:

flm serve gemma3:4b --embed 1 # Load the embedding model (embed-gemma:300m) in the background, with concurrent LLM loading (gemma3:4b).

Send file(s) to POST /v1/embeddings via any OpenAI Client or Open WebUI.

see more API details here → /v1/embeddings/

Example 1: OpenAI Client

from openai import OpenAI

client = OpenAI(
   base_url="http://localhost:52625/v1", # FastFlowLM's local API endpoint
   api_key="flm", # Dummy key (FastFlowLM doesn't require authentication)
)

resp = client.embeddings.create(
   model="embed-gemma",
   input="Hi, everyone!"
)

print(resp.data[0].embedding)

Example 2: Open WebUI

1. Follow Open WebUI setup guide.
2. In the bottom-left corner, click User icon, then select Settings.
3. In the bottom panel, open Admin Settings.
4. In the left sidebar, navigate to Documents.
5. Set Embedding Model Engine to OpenAI.
6. Enter:

   API Base URL: http://localhost:52625/v1 (Open WebUI Desktop) or http://host.docker.internal:52625/v1 (Open WebUI in Docker)
   API KEY: flm (any value works)
   Embedding Model: embed-gemma:300m

7. Save the setting.
8. Follow the RAG + FastFlowLM example to launch your Local Private Database with RAG all powered by FLM.

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