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

Libraries

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="inspirebek/qwen3-4b-uzbek-v2-GGUF",
	filename="qwen3-4b-uzbek-v2-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Ollama
How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Ollama:
```
ollama run hf.co/inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for inspirebek/qwen3-4b-uzbek-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for inspirebek/qwen3-4b-uzbek-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for inspirebek/qwen3-4b-uzbek-v2-GGUF to start chatting

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M
```

Lemonade

How to use inspirebek/qwen3-4b-uzbek-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-4b-uzbek-v2-GGUF-Q4_K_M

List all available models

lemonade list

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

inspirebek

docs: add model card

32c24b3 verified about 2 months ago

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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: gguf
pipeline_tag: text-generation
base_model: inspirebek/qwen3-4b-uzbek-v2
tags:
  - uzbek
  - qwen3
  - quantized
  - gguf
  - llama.cpp
  - ollama

qwen3-4b-uzbek-v2-gguf

gguf suite for inspirebek/qwen3-4b-uzbek-v2. cpu / apple silicon / vulkan / rocm via llama.cpp, ollama, lm studio, etc.

files

quant	size	notes
`f16`	8.8 gb	reference fp16
`Q8_0`	4.7 gb	near-lossless
`Q6_K`	3.6 gb	recommended for quality
`Q5_K_M`	3.2 gb	balanced
`Q5_K_S`	3.1 gb	slightly lighter
`Q4_K_M`	2.7 gb	recommended for most users
`Q4_K_S`	2.6 gb	smaller, slight quality loss
`Q3_K_M`	2.2 gb	aggressive
`Q2_K`	1.8 gb	edge / low-ram only

usage

llama.cpp:

llama-cli -m qwen3-4b-uzbek-v2-q4_k_m.gguf -p "Salom! Qalaysan?" -cnv

ollama:

ollama run hf.co/inspirebek/qwen3-4b-uzbek-v2-GGUF:Q4_K_M

quantization

converted from the bf16 merged model via llama.cpp's convert_hf_to_gguf.py → llama-quantize. no calibration data (k-quants are statistics-only).

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-GGUF

qwen3-4b-uzbek-v2-gguf

files

usage

quantization

datasets

sibling formats