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"

apepkuss79 commited on May 1, 2025

Commit

9bcdc2d

verified ·

1 Parent(s): a9e76e3

Update README.md

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README.md +107 -85

README.md CHANGED Viewed

@@ -1,86 +1,108 @@
----
-base_model: Qwen/Qwen3-1.7B
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
-model_creator: Qwen
-model_name: Qwen3-1.7B
-quantized_by: Second State Inc.
-language:
-- en
-pipeline_tag: text-generation
-tags:
-- chat
----
-<!-- header start -->
-<!-- 200823 -->
-<div style="width: auto; margin-left: auto; margin-right: auto">
-<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
-</div>
-<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
-<!-- header end -->
-# Qwen3-1.7B-GGUF
-## Original Model
-[Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
-## Run with LlamaEdge
-- LlamaEdge version: [v0.17.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.17.0) and above
-- Prompt template
-  - Prompt type: `chatml`
-  - Prompt string
-    ```text
-    <|im_start|>system
-    {system_message}<|im_end|>
-    <|im_start|>user
-    {prompt}<|im_end|>
-    <|im_start|>assistant
-    ```
-- Context size: `128000`
-- Run as LlamaEdge service
-  ```bash
-  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
-  ```bash
-  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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-Q4_K_S.gguf) | Q4_K_S | 4 | 1.24 GB| small, greater quality loss |
-| [Qwen3-1.7B-Q5_0.gguf](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-Q6_K.gguf)     | Q6_K   | 6 | 1.67 GB| very large, extremely low quality loss |
-| [Qwen3-1.7B-Q8_0.gguf](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-f16.gguf)       | f16    | 16 | 4.07 GB| |
 *Quantized with llama.cpp b5097*

+---
+base_model: Qwen/Qwen3-1.7B
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
+model_creator: Qwen
+model_name: Qwen3-1.7B
+quantized_by: Second State Inc.
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+# Qwen3-1.7B-GGUF
+## Original Model
+[Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+## Run with LlamaEdge
+- LlamaEdge version:
+  - Thinking: [v0.17.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.17.0) and above
+  - No Thinking: [v0.18.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.18.2)
+- Prompt template
+  - Prompt type: `chatml` (for thinking)
+  - Prompt string
+    ```text
+    <|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
+    ```text
+    <|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
+  ```bash
+  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
+  ```bash
+  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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-Q4_K_S.gguf) | Q4_K_S | 4 | 1.24 GB| small, greater quality loss |
+| [Qwen3-1.7B-Q5_0.gguf](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-Q6_K.gguf)     | Q6_K   | 6 | 1.67 GB| very large, extremely low quality loss |
+| [Qwen3-1.7B-Q8_0.gguf](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/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](https://huggingface.co/second-state/Qwen3-1.7B-GGUF/blob/main/Qwen3-1.7B-f16.gguf)       | f16    | 16 | 4.07 GB| |
 *Quantized with llama.cpp b5097*