Instructions to use ps1x/ha-russian-function-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ps1x/ha-russian-function-gemma with Transformers:

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

pipe = pipeline("text-generation", model="ps1x/ha-russian-function-gemma")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ps1x/ha-russian-function-gemma")
model = AutoModelForCausalLM.from_pretrained("ps1x/ha-russian-function-gemma")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ps1x/ha-russian-function-gemma with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ps1x/ha-russian-function-gemma"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ps1x/ha-russian-function-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ps1x/ha-russian-function-gemma

SGLang

How to use ps1x/ha-russian-function-gemma 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 "ps1x/ha-russian-function-gemma" \
    --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": "ps1x/ha-russian-function-gemma",
		"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 "ps1x/ha-russian-function-gemma" \
        --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": "ps1x/ha-russian-function-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ps1x/ha-russian-function-gemma with Docker Model Runner:
```
docker model run hf.co/ps1x/ha-russian-function-gemma
```

ha-russian-function-gemma

A specialized fine-tuned version of google/functiongemma-270m-it designed for direct, telegraphic Russian home automation commands.

Model Details

Architecture: Gemma-2
Parameters: 270M
Fine-tuning Focus: Russian Language, Short/Telegraphic commands, Home Assistant Tool Calling.
Tools Supported: set_device_state, set_device_value, get_device_status.

Usage Pattern

The model expects a developer role containing the tool definitions and a user role with telegraphic Russian.

Input Example: "включи свет полки"

Expected Output: <start_function_call>call:set_device_state{entity_id: "light.shelves", service: "turn_on"}<end_function_call>

Training Data

Fine-tuned on a custom dataset of ~1400 Russian home automation scenarios spanning climate, lighting, and sensor queries.

Downloads last month: 317

Model tree for ps1x/ha-russian-function-gemma

Base model

google/functiongemma-270m-it

Finetuned

(409)

this model

Finetunes

4 models