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

Libraries

How to use idrock/aruz with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("idrock/aruz")
model = AutoModelForCausalLM.from_pretrained("idrock/aruz", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use idrock/aruz with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/idrock/aruz

SGLang

How to use idrock/aruz 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 "idrock/aruz" \
    --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": "idrock/aruz",
		"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 "idrock/aruz" \
        --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": "idrock/aruz",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Aruz

A small, fast Uzbek (Latin script) language model for tool calling and for answering from retrieved text. Replies are short and immediate, with no chain-of-thought. It is tuned for these tasks, not for maths, code or open-ended knowledge questions.

Built from google/gemma-4-E2B-it: a new 32,768-token Uzbek tokenizer, continued pretraining on Uzbek text, then fine-tuning for tool use, grounded answers and instruction following. 2.2 B parameters, bfloat16, text only.

Usage

import json, torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("idrock/aruz")
model = AutoModelForCausalLM.from_pretrained("idrock/aruz", dtype=torch.bfloat16, device_map="auto")

def tool(name, description, properties):
    return {"type": "function", "function": {"name": name, "description": description,
            "parameters": {"type": "object", "properties": properties, "required": list(properties)}}}

tools = [
    tool("search_knowledge_base",
         "bilimlar bazasidan tarif, xizmat, protsedura va limitlar haqida ma'lumot qidirish",
         {"query": {"type": "string"},
          "topic": {"type": "string", "enum": ["tarif", "xizmat", "protsedura", "limit"]}}),
    tool("get_weather", "shahar bo'yicha ob-havo ma'lumotini olish",
         {"city": {"type": "string"}, "date": {"type": "string"}}),
    tool("set_reminder", "eslatma qo'yish", {"time": {"type": "string"}, "text": {"type": "string"}}),
]

def reply(messages):
    inputs = tok.apply_chat_template(messages, tools=tools, add_generation_prompt=True,
                                     return_tensors="pt", return_dict=True).to(model.device)
    out = model.generate(**inputs, max_new_tokens=120, do_sample=False)
    return tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=False)

messages = [{"role": "user", "content": "Registonga talabalar uchun chipta qancha turadi?"}]
print(reply(messages))   # <|tool_call>call:search_knowledge_base{query:<|"|>...<|"|>,topic:<|"|>...<|"|>}<tool_call|>

chunks = [{"id": "d1", "title": "Kirish narxi",
           "text": "Registon ansambliga kirish chiptasi kattalar uchun 65000 so'm, talabalar uchun 30000 so'm turadi."}]
messages += [
    {"role": "assistant", "tool_calls": [{"id": "c1", "type": "function", "function": {
        "name": "search_knowledge_base", "arguments": {"query": "Registon chipta narxi", "topic": "tarif"}}}]},
    {"role": "tool", "tool_call_id": "c1", "name": "search_knowledge_base",
     "content": json.dumps({"chunks": chunks}, ensure_ascii=False)},
]
print(reply(messages))

Keep search_knowledge_base exactly as above and declare it alongside other tools — declared on its own, its name comes out garbled.

With vLLM, don't use --tool-call-parser gemma4; it corrupts tool names under this tokenizer. Render the prompt with the chat template, call /v1/completions with skip_special_tokens: false, and parse the tool call from the text.

License

Apache 2.0. Aruz is a modified version of Google's Gemma 4 E2B (vocabulary replaced, vision and audio encoders removed, all weights further trained), which is released under the Apache 2.0 license. Not affiliated with or endorsed by Google.