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

Libraries

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

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

pipe = pipeline("text-generation", model="inspirebek/qwen3-4b-uzbek-v2-awq")
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-awq")
model = AutoModelForCausalLM.from_pretrained("inspirebek/qwen3-4b-uzbek-v2-awq")
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 inspirebek/qwen3-4b-uzbek-v2-awq 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-awq"
# 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-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

qwen3-4b-uzbek-v2-awq / README.md

inspirebek

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metadata

language:
  - uz
  - en
license: cc-by-nc-4.0
datasets:
  - yakhyo/uz-wiki
  - tahrirchi/uz-books-v2
  - tahrirchi/uz-crawl
  - saillab/alpaca_uzbek_taco
  - behbudiy/alpaca-cleaned-uz
  - UAzimov/uzbek-instruct-llm
  - CohereLabs/aya_collection_language_split
  - med-alex/qa_mt_ru_to_uzn
  - med-alex/qa_mt_tr_to_uzn
library_name: transformers
pipeline_tag: text-generation
base_model: inspirebek/qwen3-4b-uzbek-v2
tags:
  - uzbek
  - qwen3
  - quantized
  - 4-bit
  - awq

qwen3-4b-uzbek-v2-awq

awq 4-bit activation-aware quant (~3.4 gb) of inspirebek/qwen3-4b-uzbek-v2. fast gpu inference via vllm / tgi / transformers.

usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("inspirebek/qwen3-4b-uzbek-v2-awq")
model = AutoModelForCausalLM.from_pretrained(
    "inspirebek/qwen3-4b-uzbek-v2-awq",
    device_map="auto",
)

with vllm:

vllm serve inspirebek/qwen3-4b-uzbek-v2-awq --quantization awq --dtype float16

quantization

method: awq (autoawq 0.2.9, gemm version)
w_bit=4, q_group_size=128, zero_point=True
calibration: 128 uzbek samples (2048 tokens each) from fluency.jsonl

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.

inspirebek
/

qwen3-4b-uzbek-v2-awq

qwen3-4b-uzbek-v2-awq

usage

quantization

datasets

sibling formats