Instructions to use g023/qwen3-tiny-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use g023/qwen3-tiny-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="g023/qwen3-tiny-v2",
	filename="Qwen3-g023-tiny-v2-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use g023/qwen3-tiny-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf g023/qwen3-tiny-v2:Q4_K_M

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use g023/qwen3-tiny-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "g023/qwen3-tiny-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "g023/qwen3-tiny-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M

Ollama
How to use g023/qwen3-tiny-v2 with Ollama:
```
ollama run hf.co/g023/qwen3-tiny-v2:Q4_K_M
```

Unsloth Studio

How to use g023/qwen3-tiny-v2 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 g023/qwen3-tiny-v2 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 g023/qwen3-tiny-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for g023/qwen3-tiny-v2 to start chatting

How to use g023/qwen3-tiny-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf g023/qwen3-tiny-v2: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": "g023/qwen3-tiny-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use g023/qwen3-tiny-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use g023/qwen3-tiny-v2 with Docker Model Runner:
```
docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M
```

Lemonade

How to use g023/qwen3-tiny-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull g023/qwen3-tiny-v2:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-tiny-v2-Q4_K_M

List all available models

lemonade list

g023 commited on Mar 30

Commit

1db2f0f

verified ·

1 Parent(s): 156c198

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -9

README.md CHANGED Viewed

@@ -29,7 +29,7 @@ Created through innovative layer surgery combining multi-swap, interpolation, an
 | Quantization | Bits/Weight | Description | Download |
 |:---:|:---:|:---|:---:|
-| **Q8_0** | 8.00 | Highest quality, virtually lossless | [Qwen3-g023-tiny-v2-Q8_0.gguf](./Qwen3-g023-tiny-v2-Q8_0.gguf) |
 | **Q6_K** | 6.57 | Excellent quality, good compression | [Qwen3-g023-tiny-v2-Q6_K.gguf](./Qwen3-g023-tiny-v2-Q6_K.gguf) |
 | **Q4_K_M** | 4.85 | Good balance of quality and size | [Qwen3-g023-tiny-v2-Q4_K_M.gguf](./Qwen3-g023-tiny-v2-Q4_K_M.gguf) |
 | **Q3_K_M** | 3.91 | High compression, moderate quality loss | [Qwen3-g023-tiny-v2-Q3_K_M.gguf](./Qwen3-g023-tiny-v2-Q3_K_M.gguf) |
@@ -93,12 +93,20 @@ Evaluated using a comprehensive test suite with 17 factual questions, 2 completi
 ```bash
 # Download the GGUF and create from Modelfile
 cat > Modelfile << 'EOF'
-FROM ./Qwen3-g023-tiny-v2-Q4_K_M.gguf
-PARAMETER temperature 0.6
 PARAMETER top_p 0.95
-PARAMETER top_k 20
-PARAMETER min_p 0.0
 TEMPLATE """{{- if .System }}
 <|im_start|>system
@@ -126,16 +134,16 @@ ollama run qwen3-tiny-v2
 ```bash
 # Interactive chat
-llama-cli -m Qwen3-g023-tiny-v2-Q4_K_M.gguf \
   --chat-template chatml -cnv
 # Thinking mode
-llama-cli -m Qwen3-g023-tiny-v2-Q4_K_M.gguf \
   -p "<|im_start|>user\nExplain quantum computing<|im_end|>\n<|im_start|>assistant\n<think>\n" \
   -n 512
 # Non-thinking mode
-llama-cli -m Qwen3-g023-tiny-v2-Q4_K_M.gguf \
   -p "<|im_start|>user\n/no_think What is 2+2?<|im_end|>\n<|im_start|>assistant\n" \
   -n 128
 ```
@@ -145,7 +153,7 @@ llama-cli -m Qwen3-g023-tiny-v2-Q4_K_M.gguf \
 ```python
 from llama_cpp import Llama
-model = Llama("Qwen3-g023-tiny-v2-Q4_K_M.gguf", n_ctx=4096)
 response = model.create_chat_completion(
     messages=[
         {"role": "system", "content": "You are a helpful assistant."},

 | Quantization | Bits/Weight | Description | Download |
 |:---:|:---:|:---|:---:|
+| **Q8_0** | 8.00 | Highest quality, virtually lossless (USE THIS ONE) | [Qwen3-g023-tiny-v2-Q8_0.gguf](./Qwen3-g023-tiny-v2-Q8_0.gguf) |
 | **Q6_K** | 6.57 | Excellent quality, good compression | [Qwen3-g023-tiny-v2-Q6_K.gguf](./Qwen3-g023-tiny-v2-Q6_K.gguf) |
 | **Q4_K_M** | 4.85 | Good balance of quality and size | [Qwen3-g023-tiny-v2-Q4_K_M.gguf](./Qwen3-g023-tiny-v2-Q4_K_M.gguf) |
 | **Q3_K_M** | 3.91 | High compression, moderate quality loss | [Qwen3-g023-tiny-v2-Q3_K_M.gguf](./Qwen3-g023-tiny-v2-Q3_K_M.gguf) |
 ```bash
 # Download the GGUF and create from Modelfile
 cat > Modelfile << 'EOF'
+FROM ./Qwen3-g023-tiny-v2-Q8_0.gguf
+PARAMETER temperature 1.0
 PARAMETER top_p 0.95
+PARAMETER top_k 45
+PARAMETER min_p 0.1
+PARAMETER num_ctx 40000
+PARAMETER mirostat 2
+PARAMETER mirostat_tau 5.0
+PARAMETER mirostat_eta 0.1
+PARAMETER repeat_last_n 16384
+PARAMETER repeat_penalty 1.1
+PARAMETER presence_penalty 0.5
+PARAMETER frequency_penalty 1.0
 TEMPLATE """{{- if .System }}
 <|im_start|>system
 ```bash
 # Interactive chat
+llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
   --chat-template chatml -cnv
 # Thinking mode
+llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
   -p "<|im_start|>user\nExplain quantum computing<|im_end|>\n<|im_start|>assistant\n<think>\n" \
   -n 512
 # Non-thinking mode
+llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
   -p "<|im_start|>user\n/no_think What is 2+2?<|im_end|>\n<|im_start|>assistant\n" \
   -n 128
 ```
 ```python
 from llama_cpp import Llama
+model = Llama("Qwen3-g023-tiny-v2-Q8_0.gguf", n_ctx=4096)
 response = model.create_chat_completion(
     messages=[
         {"role": "system", "content": "You are a helpful assistant."},