Instructions to use xpol555/sm-coder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xpol555/sm-coder-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="xpol555/sm-coder-gguf",
	filename="sm-coder-1.5b-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use xpol555/sm-coder-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 xpol555/sm-coder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf xpol555/sm-coder-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 xpol555/sm-coder-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf xpol555/sm-coder-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 xpol555/sm-coder-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf xpol555/sm-coder-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 xpol555/sm-coder-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf xpol555/sm-coder-gguf:Q4_K_M

Use Docker

docker model run hf.co/xpol555/sm-coder-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use xpol555/sm-coder-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xpol555/sm-coder-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": "xpol555/sm-coder-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xpol555/sm-coder-gguf:Q4_K_M

Ollama
How to use xpol555/sm-coder-gguf with Ollama:
```
ollama run hf.co/xpol555/sm-coder-gguf:Q4_K_M
```

Unsloth Studio

How to use xpol555/sm-coder-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 xpol555/sm-coder-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 xpol555/sm-coder-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for xpol555/sm-coder-gguf to start chatting

How to use xpol555/sm-coder-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf xpol555/sm-coder-gguf: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": "xpol555/sm-coder-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use xpol555/sm-coder-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 xpol555/sm-coder-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 xpol555/sm-coder-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use xpol555/sm-coder-gguf with Docker Model Runner:
```
docker model run hf.co/xpol555/sm-coder-gguf:Q4_K_M
```

Lemonade

How to use xpol555/sm-coder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull xpol555/sm-coder-gguf:Q4_K_M

Run and chat with the model

lemonade run user.sm-coder-gguf-Q4_K_M

List all available models

lemonade list

SM Coder 1.5B — GGUF

A fine-tuned coding assistant for SuperMongo (SM) macro development, packaged as a quantized GGUF file for use with Ollama or llama.cpp.

File	Quant	Size
`sm-coder-1.5b-q4_k_m.gguf`	Q4_K_M	~1 GB

Quick Start (Ollama)

# create the model from the bundled Modelfile
ollama create sm-coder -f Modelfile.finetuned

# chat
ollama run sm-coder "Write an SM macro to overplot error bars"

Base Model

Qwen/Qwen2.5-Coder-1.5B-Instruct, fine-tuned on a curated dataset of SM macros and related examples collected over ~20 years of real scientific usage.

What Is SuperMongo

SuperMongo is a plotting and data-analysis environment widely used in astronomy and scientific workflows.

Reference: https://www.astro.princeton.edu/~rhl/sm/

Intended Use

Writing and refactoring SuperMongo macros.
Translating plotting requests into SM macro skeletons.
Helping document and modernize legacy macro collections.

Limitations

Not a general-purpose chatbot.
Not an authoritative source on non-SM domains.
Always validate generated macros against your SM version and local macro library before use.

Example Prompts

"Write an SM macro that draws concentric circles given a center, starting radius, step, and count."
"Write an SM macro that reads two columns from a file and plots them with auto-scaled limits."
"Refactor this SM macro into smaller reusable helpers."

Example Output

The model produces macros in standard SM syntax (positional $1 $2 … parameters, set for vectors, define for scalars):

draw_circle 4   ## draw a circle centred at ($1,$2) radius $3 with $4 points
        set _th = 2*PI*indgen($4)/$4
        set _cx = $1 + $3*COS(_th)
        set _cy = $2 + $3*SIN(_th)
        connect _cx _cy

quickplot 1     ## read two-column file $1, auto-scale, and plot
        data $1
        read {x 1 y 2}
        limits x y
        erase
        box
        connect x y
        xlabel Column 1
        ylabel Column 2

oploterr 4      ## overplot error bars on current axes for x=$1 y=$2 ey=$3
        set _ylo = $2 - $3
        set _yhi = $2 + $3
        error_y $1 $2 _ylo _yhi