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LatestGoogle DeepMind270m-it
Google DeepMindText / LLM

Google Releases Compact FunctionGemma Model

The new 270-million-parameter model from Google DeepMind is fine-tuned specifically for reliable function calling and tool use.

Oct 8, 2025
NotableGemma
FunctionGemma 270M IT

Google has introduced FunctionGemma, a new family of open-weights models specifically designed for function calling. The first release is a compact 270-million-parameter instruction-tuned model, aimed at developers building AI agents that need to reliably interact with external tools and APIs.

Function calling, or tool use, allows a large language model to go beyond generating text by invoking predefined code. This enables applications that can fetch real-time data, interact with databases, or control other software. Creating small, efficient models for this task is a significant step toward building more capable and practical AI systems.

Why it Matters

Based on the Gemma architecture, FunctionGemma is optimized for performance in this specialized domain. Its small size makes it a compelling choice for on-device applications or scenarios where computational resources are limited. This provides an alternative to relying on larger, general-purpose models or proprietary APIs for tool-use capabilities.

For developers, a dedicated, open-weights function-calling model offers more control and cost-efficiency. It can be fine-tuned for specific toolsets and integrated directly into applications for tasks like building conversational agents that access real-time information or creating natural language interfaces for existing software.

The FunctionGemma 270M IT model is available now on Hugging Face under the Gemma license, which permits commercial use and distribution subject to its terms.

Sources

  • google/functiongemma-270m-it

    Hugging Face

    Visit
OlderEPFL Releases SVI for Streaming Image-to-VideoEPFL VITA · Image → Video · last yearNewerAlibaba Releases Qwen3-VL, an 8B Open-Source Vision ModelQwen · Alibaba · Vision-Language · last year

Get the model

Hugging Face

Specs

Parameters270M
Context window33K tokens
Size500 MB
PrecisionBF16
ArchitectureGemma3ForCausalLM
LicenseGEMMA
Downloads13.2K
Likes1.1K

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    Any modern laptop · 8 GB RAM

    1.7 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    1.5 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    1.4 GB
Your machine
GGUF builds
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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