Instructions to use levelplaystudio/echo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use levelplaystudio/echo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="levelplaystudio/echo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("levelplaystudio/echo")
model = AutoModelForCausalLM.from_pretrained("levelplaystudio/echo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use levelplaystudio/echo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "levelplaystudio/echo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "levelplaystudio/echo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/levelplaystudio/echo

SGLang

How to use levelplaystudio/echo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "levelplaystudio/echo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "levelplaystudio/echo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "levelplaystudio/echo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "levelplaystudio/echo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use levelplaystudio/echo with Docker Model Runner:
```
docker model run hf.co/levelplaystudio/echo
```

echo / README.md

Lakshychaurasia

Update README.md

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---
license: apache-2.0
base_model: google/functiongemma-270m-it
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - function-calling
  - tool-use
  - on-device
  - edge
  - functiongemma
  - personal-assistant
  - synthetic-data
  - safetensors
model-index:
  - name: echo-functiongemma-270m
    results: []
---
# ECHO FunctionGemma 270M

ECHO is a small on-device personal assistant model. It reads a user request and chooses the right tool, with arguments, to carry it out on the local machine. It is a fine-tune of [`google/functiongemma-270m-it`](https://huggingface.co/google/functiongemma-270m-it) and is built to run without a cloud connection on hardware such as a Raspberry Pi 4B (4GB) or a Windows desktop.

**Creators:** Lakshy M. Chaurasia and Pratigya M. Chaurasia, [Level Play Studio](#).

## Model details

| 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:

| 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. |
| `get_current_time` | Return the local time. |
| `get_current_date` | Return the date and day of the week. |
| `get_system_info` | CPU, RAM, disk, and OS diagnostics. |
| `volume_manager` | Set, mute, unmute, increase, or decrease audio volume (0–100). |
| `take_screenshot` | Capture the full screen, the active window, or a region. |
| `file_manager` | List, read, create, write, append, search, rename, move, copy, delete, and inspect files and folders. Includes multi-turn navigation (search, list, read, then respond). |
| `response` | Conversational replies and final answers after a tool call. Includes ECHO's philosophical persona and creator information. |
| `web_search` | Search the web for current news, facts, and tutorials. |

## 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.

| 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

```
@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`](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.

	---
	license: apache-2.0
	base_model: google/functiongemma-270m-it
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- function-calling
	- tool-use
	- on-device
	- edge
	- functiongemma
	- personal-assistant
	- synthetic-data
	- safetensors
	model-index:
	- name: echo-functiongemma-270m
	results: []
	---

	# ECHO FunctionGemma 270M

	ECHO is a small on-device personal assistant model. It reads a user request and chooses the right tool, with arguments, to carry it out on the local machine. It is a fine-tune of [`google/functiongemma-270m-it`](https://huggingface.co/google/functiongemma-270m-it) and is built to run without a cloud connection on hardware such as a Raspberry Pi 4B (4GB) or a Windows desktop.

	Creators: Lakshy M. Chaurasia and Pratigya M. Chaurasia, [Level Play Studio](#).

	## Model details

	\| 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:

	\| 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. \|
	\| `get_current_time` \| Return the local time. \|
	\| `get_current_date` \| Return the date and day of the week. \|
	\| `get_system_info` \| CPU, RAM, disk, and OS diagnostics. \|
	\| `volume_manager` \| Set, mute, unmute, increase, or decrease audio volume (0–100). \|
	\| `take_screenshot` \| Capture the full screen, the active window, or a region. \|
	\| `file_manager` \| List, read, create, write, append, search, rename, move, copy, delete, and inspect files and folders. Includes multi-turn navigation (search, list, read, then respond). \|
	\| `response` \| Conversational replies and final answers after a tool call. Includes ECHO's philosophical persona and creator information. \|
	\| `web_search` \| Search the web for current news, facts, and tutorials. \|

	## 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.

	\| 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

	```
	@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`](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.