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
```

inspirebek commited on Apr 20

Commit

d4f8635

verified ·

1 Parent(s): 93de1df

docs: add model card

Browse files

Files changed (1) hide show

README.md +30 -1

README.md CHANGED Viewed

@@ -2,7 +2,17 @@
 language:
 - uz
 - en
-license: apache-2.0
 library_name: transformers
 pipeline_tag: text-generation
 base_model: inspirebek/qwen3-4b-uzbek-v2
@@ -42,6 +52,25 @@ vllm serve inspirebek/qwen3-4b-uzbek-v2-awq --quantization awq --dtype float16
 - `w_bit=4, q_group_size=128, zero_point=True`
 - calibration: 128 uzbek samples (2048 tokens each) from `fluency.jsonl`
 ## sibling formats
 - [`inspirebek/qwen3-4b-uzbek-v2`](https://huggingface.co/inspirebek/qwen3-4b-uzbek-v2)

 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
 - `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):**
+- [`yakhyo/uz-wiki`](https://huggingface.co/datasets/yakhyo/uz-wiki) · MIT
+- [`tahrirchi/uz-books-v2`](https://huggingface.co/datasets/tahrirchi/uz-books-v2) · MIT
+- [`tahrirchi/uz-crawl`](https://huggingface.co/datasets/tahrirchi/uz-crawl) · Apache-2.0
+**stage b — instruct (sft):**
+- [`saillab/alpaca_uzbek_taco`](https://huggingface.co/datasets/saillab/alpaca_uzbek_taco) · CC-BY-NC-4.0
+- [`behbudiy/alpaca-cleaned-uz`](https://huggingface.co/datasets/behbudiy/alpaca-cleaned-uz) · CC-BY-4.0
+- [`UAzimov/uzbek-instruct-llm`](https://huggingface.co/datasets/UAzimov/uzbek-instruct-llm) · Apache-2.0
+- [`CohereLabs/aya_collection_language_split`](https://huggingface.co/datasets/CohereLabs/aya_collection_language_split) · Apache-2.0
+- [`med-alex/qa_mt_ru_to_uzn`](https://huggingface.co/datasets/med-alex/qa_mt_ru_to_uzn) · unspecified
+- [`med-alex/qa_mt_tr_to_uzn`](https://huggingface.co/datasets/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.
 ## sibling formats
 - [`inspirebek/qwen3-4b-uzbek-v2`](https://huggingface.co/inspirebek/qwen3-4b-uzbek-v2)