Instructions to use sinatras/qwen3-1.7b-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sinatras/qwen3-1.7b-split 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 sinatras/qwen3-1.7b-split:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sinatras/qwen3-1.7b-split:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sinatras/qwen3-1.7b-split:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sinatras/qwen3-1.7b-split: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 sinatras/qwen3-1.7b-split:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sinatras/qwen3-1.7b-split: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 sinatras/qwen3-1.7b-split:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinatras/qwen3-1.7b-split:Q4_K_M

Use Docker

docker model run hf.co/sinatras/qwen3-1.7b-split:Q4_K_M

LM Studio
Jan
Ollama
How to use sinatras/qwen3-1.7b-split with Ollama:
```
ollama run hf.co/sinatras/qwen3-1.7b-split:Q4_K_M
```
Unsloth Desktop

How to use sinatras/qwen3-1.7b-split with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinatras/qwen3-1.7b-split: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": "sinatras/qwen3-1.7b-split:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sinatras/qwen3-1.7b-split with Docker Model Runner:
```
docker model run hf.co/sinatras/qwen3-1.7b-split:Q4_K_M
```

Lemonade

How to use sinatras/qwen3-1.7b-split with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sinatras/qwen3-1.7b-split:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-1.7b-split-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sinatras/qwen3-1.7b-split with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinatras/qwen3-1.7b-split: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 sinatras/qwen3-1.7b-split:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sinatras/qwen3-1.7b-split with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinatras/qwen3-1.7b-split: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 "sinatras/qwen3-1.7b-split: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-split

Qwen3 1.7B GGUF artifacts used by the playground wllama preset.

These files are the GGUF artifacts used by the local Transformers.js playground wllama CPU presets. Large files are kept under quantization subdirectories so browser clients can request the first shard URL and expand the remaining shards.

Source And License

Source model/artifact: ggml-org/Qwen3-1.7B-GGUF
License: Apache-2.0, inherited from the source model/artifact.

The GGUF conversion, quantization, and splitting steps do not change the upstream model license.

Downloads last month: 9

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for sinatras/qwen3-1.7b-split

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

ggml-org/Qwen3-1.7B-GGUF

Quantized

(1)

this model