Instructions to use inspirebek/qwen3-4b-uzbek-v2-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inspirebek/qwen3-4b-uzbek-v2-bnb-4bit with Transformers:

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

pipe = pipeline("text-generation", model="inspirebek/qwen3-4b-uzbek-v2-bnb-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inspirebek/qwen3-4b-uzbek-v2-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained("inspirebek/qwen3-4b-uzbek-v2-bnb-4bit")
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 inspirebek/qwen3-4b-uzbek-v2-bnb-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inspirebek/qwen3-4b-uzbek-v2-bnb-4bit

SGLang

How to use inspirebek/qwen3-4b-uzbek-v2-bnb-4bit 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 "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit" \
    --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": "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit",
		"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 "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit" \
        --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": "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inspirebek/qwen3-4b-uzbek-v2-bnb-4bit with Docker Model Runner:
```
docker model run hf.co/inspirebek/qwen3-4b-uzbek-v2-bnb-4bit
```

qwen3-4b-uzbek-v2-bnb-4bit

bitsandbytes nf4 4-bit quant (~3.4 gb) of inspirebek/qwen3-4b-uzbek-v2. nvidia gpu only; easiest hf-native 4-bit load.

usage

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)
tok = AutoTokenizer.from_pretrained("inspirebek/qwen3-4b-uzbek-v2-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained(
    "inspirebek/qwen3-4b-uzbek-v2-bnb-4bit",
    quantization_config=bnb,
    device_map="auto",
)

quantization

method: bitsandbytes nf4 (4-bit normalfloat)
double quantization: enabled
compute dtype: bfloat16

datasets

stage a — fluency (continued pretraining):

stage b — instruct (sft):

saillab/alpaca_uzbek_taco · CC-BY-NC-4.0
behbudiy/alpaca-cleaned-uz · CC-BY-4.0
UAzimov/uzbek-instruct-llm · Apache-2.0
CohereLabs/aya_collection_language_split · Apache-2.0
med-alex/qa_mt_ru_to_uzn · unspecified
med-alex/qa_mt_tr_to_uzn · unspecified

⚠️ licensing note: saillab/alpaca_uzbek_taco is cc-by-nc-4.0, which restricts commercial use of derivative models. downstream users who need a fully permissive license should retrain without that subset.