Instructions to use second-state/Qwen3-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use second-state/Qwen3-1.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Qwen3-1.7B-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 second-state/Qwen3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Qwen3-1.7B-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 second-state/Qwen3-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Qwen3-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Qwen3-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Qwen3-1.7B-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": "second-state/Qwen3-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Qwen3-1.7B-GGUF:Q4_K_M

Ollama
How to use second-state/Qwen3-1.7B-GGUF with Ollama:
```
ollama run hf.co/second-state/Qwen3-1.7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/Qwen3-1.7B-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 second-state/Qwen3-1.7B-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 second-state/Qwen3-1.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/Qwen3-1.7B-GGUF to start chatting

How to use second-state/Qwen3-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "second-state/Qwen3-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use second-state/Qwen3-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Qwen3-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Qwen3-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Qwen3-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use second-state/Qwen3-1.7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Qwen3-1.7B-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 second-state/Qwen3-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use second-state/Qwen3-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Qwen3-1.7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "second-state/Qwen3-1.7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-1.7B-GGUF

Original Model

Qwen/Qwen3-1.7B

Run with LlamaEdge

LlamaEdge version:
- Thinking: v0.17.0 and above
- No Thinking: v0.18.2

Prompt template

Prompt type: chatml (for thinking)

Prompt string

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Prompt type: qwen3-no-think (for no thinking)

Prompt string

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message_1}<|im_end|>
<|im_start|>assistant
{assistant_message_1}<|im_end|>
<|im_start|>user
{user_message_2}<|im_end|>
<|im_start|>assistant
<think>

</think>

Context size: 128000

Run as LlamaEdge service

wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen3-1.7B-Q5_K_M.gguf \
  llama-api-server.wasm \
  --model-name Qwen3-1.7B \
  --prompt-template chatml \
  --ctx-size 128000

Run as LlamaEdge command app

wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen3-1.7B-Q5_K_M.gguf \
  llama-chat.wasm \
  --prompt-template chatml \
  --ctx-size 128000

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
Qwen3-1.7B-Q2_K.gguf	Q2_K	2	880 MB	smallest, significant quality loss - not recommended for most purposes
Qwen3-1.7B-Q3_K_L.gguf	Q3_K_L	3	1.14 GB	small, substantial quality loss
Qwen3-1.7B-Q3_K_M.gguf	Q3_K_M	3	1.07 GB	very small, high quality loss
Qwen3-1.7B-Q3_K_S.gguf	Q3_K_S	3	1.00 GB	very small, high quality loss
Qwen3-1.7B-Q4_0.gguf	Q4_0	4	1.23 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Qwen3-1.7B-Q4_K_M.gguf	Q4_K_M	4	1.28 GB	medium, balanced quality - recommended
Qwen3-1.7B-Q4_K_S.gguf	Q4_K_S	4	1.24 GB	small, greater quality loss
Qwen3-1.7B-Q5_0.gguf	Q5_0	5	1.44 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Qwen3-1.7B-Q5_K_M.gguf	Q5_K_M	5	1.47 GB	large, very low quality loss - recommended
Qwen3-1.7B-Q5_K_S.gguf	Q5_K_S	5	1.44 GB	large, low quality loss - recommended
Qwen3-1.7B-Q6_K.gguf	Q6_K	6	1.67 GB	very large, extremely low quality loss
Qwen3-1.7B-Q8_0.gguf	Q8_0	8	2.17 GB	very large, extremely low quality loss - not recommended
Qwen3-1.7B-f16.gguf	f16	16	4.07 GB

Quantized with llama.cpp b5097

Downloads last month: 474

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/Qwen3-1.7B-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(348)

this model

Collection including second-state/Qwen3-1.7B-GGUF

Qwen3 GGUF Models

Collection

LlamaEdge compatible quants for Qwen3 models. • 7 items • Updated Apr 29, 2025