Instructions to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion 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 PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Use Docker

docker model run hf.co/PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Ollama
How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with Ollama:
```
ollama run hf.co/PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M
```
Unsloth Desktop

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion: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": "PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with Docker Model Runner:
```
docker model run hf.co/PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M
```

Lemonade

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-Coder-1.5B_Q4_K_M_full_line_completion-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion: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 PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion: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 "PascalABCNET/Qwen-Coder-1.5B_Q4_K_M_full_line_completion: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"

Qwen-Coder-1.5B_Q4_K_M_full_line_completion

File size: 419 Bytes

fdea0f6
 
 
 
08c88c3
fdea0f6

{
  "schemaVersion": 1,
  "modelFile": "completion_qwen_pascal_q4_k_m.gguf",
  "revision": "0335a690e63d4c499b225b84690f6627d4b36b6b",
  "sha256": "2bcc84628ae82b83f51b0956ccfae91365d061b4fdc51f352f8f96fab9fb1d3f",
  "sizeBytes": 986047968,
  "displayName": "PascalABC.NET Full Line Completion Qwen-Coder-1.5B Q4_K_M",
  "baseModel": "Qwen/Qwen2.5-Coder-1.5B",
  "quantization": "Q4_K_M",
  "createdAt": "2026-11-12"
}