levelplaystudio

echo

README

License: apache-2.0

Model details

Table with columns: Item, Value
Item	Value
Base model	`google/functiongemma-270m-it`
Parameters	~270M
Format	Safetensors (`model.safetensors`)
Language	English (Indian English context)
Task	Function calling for personal computer and assistant actions
Intended hardware	Raspberry Pi 4B (4GB), Windows desktop, edge devices
Context	Single-turn tool selection

Supported tools

The model is trained to call these 13 tools:

Table with columns: Tool, Purpose
Tool	Purpose
`music_manager`	Play, pause, resume, next, previous. Includes classic Indian film and ghazal music.
`get_weather`	Current weather and forecasts for a city or region.
`open_application`	Launch desktop apps, terminals, and utilities.
`close_application`	Close or terminate running apps.
`task_manager`	Create, list, delete, and complete tasks and reminders.

Training data

Source: synthetic. User queries and tool calls were generated with Gemini and checked against the tool schemas before use.
Size: about 5,200 examples in total, split roughly 90% train and 10% evaluation, stratified by tool.
Format: each example is a chat with a developer message (date, time, day of week, and the tool list), a user message, and an assistant tool call. Tool lists are shuffled per example.
Coverage: English only. Indian cities, names, festivals, and Windows paths. Varied styles: formal requests, short voice commands, slang, typos, and multi-sentence commands.
Validation: arguments must match the schema exactly (names, types, enum values, and the 0–100 volume range). Non-English text and duplicate queries were removed.

Evaluation

Evaluation uses a held-out split of the synthetic data. Replace the placeholders below with your measured results.

Table with columns: Metric, Score
Metric	Score
Tool-name accuracy	`XX.XX%`
Exact-arguments accuracy	`XX.XX%`
Schema-valid calls	`XX.XX%`

Synthetic evaluation does not show real-world performance. Test on real user phrasings before relying on the model.

How to use

python

from transformers import AutoTokenizer, AutoModelForCausalLM repo_id = "YOUR_USERNAME/echo-functiongemma-270m"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto") tools = [    {        "type": "function",        "function": {            "name": "volume_manager",            "description": "Adjusts host master audio volume or toggles mute status.",            "parameters": {                "type": "object",                "properties": {                    "action": {"type": "string", "description": "Volume action: 'set', 'mute', 'unmute', 'increase', 'decrease'."},                    "level": {"type": "integer", "description": "Integer audio level between 0 and 100."},                },                "required": ["action"],            },        },    },    # Add the other tools from the table above as needed.] messages = [    {"role": "developer", "content": "Current date and time given in YYYY-MM-DDTHH:MM:SS format: 2026-10-18T09:30:00\nDay of week is Sunday\nYou are a model that can do function calling with the following functions\n"},    {"role": "user", "content": "Lower the sound to 20 percent"},] inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_tensors="pt", return_dict=True)output = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=False))

Parse the generated tool call and run it through your own tool dispatcher. Do not execute model output directly without checking the tool name and arguments against the schema.

Limitations

Trained on synthetic data, so it may miss phrasings and situations that real users write.
Supports only the 13 tools listed above. Requests for other tools will not be handled correctly.
Multi-turn examples cover mainly file_manager navigation and summarizing tool output in response. Long multi-step workflows across other tools are not covered.
English only.
Music knowledge covers mainly Indian film and ghazal music from about 1950 to 1980. Verify artist and song names for other genres.
Argument values such as file paths and times should be checked before any action is taken on the device.

Safety

ECHO can open and close applications, change system volume, and capture the screen. Run actions through a confirmation step for anything destructive, and never run it with elevated permissions without review.

Citation

text

@misc{echo_functiongemma_270m,  title  = {ECHO FunctionGemma 270M},  author = {Chaurasia, Lakshy M. and Chaurasia, Pratigya M.},  year   = {2026},  note   = {Level Play Studio}}

License

This model is licensed under the Apache License, Version 2.0. See the LICENSE file for the full text.

Note: the base model google/functiongemma-270m-it is distributed under Google's Gemma terms of use, not Apache 2.0. Those terms may apply to derivative weights, so check them before you rely on Apache 2.0 for this model alone.

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Model Details

Model Provider

levelplaystudio

Model Tree

Base

google/functiongemma-270m-it

Fine-tuned

this model

Input Modalities

Text

Output Modalities