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
```

Lakshychaurasia commited on 18 days ago

Commit

00c9437

verified ·

1 Parent(s): dff8704

Update README.md

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README.md +6 -3

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ ECHO is a small on-device personal assistant model. It reads a user request and
 ## Supported tools
-The model is trained to call these 10 tools:
 | Tool | Purpose |
 | :--- | :--- |
@@ -53,6 +53,9 @@ The model is trained to call these 10 tools:
 | `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. |
 ## Training data
@@ -117,8 +120,8 @@ Parse the generated tool call and run it through your own tool dispatcher. Do no
 ## Limitations
 - Trained on synthetic data, so it may miss phrasings and situations that real users write.
-- Supports only the 10 tools listed above. Requests for other tools will not be handled correctly.
-- Single-turn tool selection. It is not trained for long multi-step workflows or for reading tool output in later turns.
 - 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.

 ## Supported tools
+The model is trained to call these 13 tools:
 | Tool | Purpose |
 | :--- | :--- |
 | `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
 ## 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.