Instructions to use Fazmin/solus_v1_maincoder-1b-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Fazmin/solus_v1_maincoder-1b-f16 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 Fazmin/solus_v1_maincoder-1b-f16:F16
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_maincoder-1b-f16:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fazmin/solus_v1_maincoder-1b-f16:F16
# Run inference directly in the terminal:
llama cli -hf Fazmin/solus_v1_maincoder-1b-f16:F16

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 Fazmin/solus_v1_maincoder-1b-f16:F16
# Run inference directly in the terminal:
./llama-cli -hf Fazmin/solus_v1_maincoder-1b-f16:F16

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 Fazmin/solus_v1_maincoder-1b-f16:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fazmin/solus_v1_maincoder-1b-f16:F16

Use Docker

docker model run hf.co/Fazmin/solus_v1_maincoder-1b-f16:F16

LM Studio
Jan

vLLM

How to use Fazmin/solus_v1_maincoder-1b-f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fazmin/solus_v1_maincoder-1b-f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fazmin/solus_v1_maincoder-1b-f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fazmin/solus_v1_maincoder-1b-f16:F16

Ollama
How to use Fazmin/solus_v1_maincoder-1b-f16 with Ollama:
```
ollama run hf.co/Fazmin/solus_v1_maincoder-1b-f16:F16
```

Unsloth Studio

How to use Fazmin/solus_v1_maincoder-1b-f16 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 Fazmin/solus_v1_maincoder-1b-f16 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 Fazmin/solus_v1_maincoder-1b-f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fazmin/solus_v1_maincoder-1b-f16 to start chatting

How to use Fazmin/solus_v1_maincoder-1b-f16 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_maincoder-1b-f16:F16

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": "Fazmin/solus_v1_maincoder-1b-f16:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Fazmin/solus_v1_maincoder-1b-f16 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_maincoder-1b-f16:F16

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 Fazmin/solus_v1_maincoder-1b-f16:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Fazmin/solus_v1_maincoder-1b-f16 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fazmin/solus_v1_maincoder-1b-f16:F16

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 "Fazmin/solus_v1_maincoder-1b-f16:F16" \
  --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"

Docker Model Runner
How to use Fazmin/solus_v1_maincoder-1b-f16 with Docker Model Runner:
```
docker model run hf.co/Fazmin/solus_v1_maincoder-1b-f16:F16
```

Lemonade

How to use Fazmin/solus_v1_maincoder-1b-f16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fazmin/solus_v1_maincoder-1b-f16:F16

Run and chat with the model

lemonade run user.solus_v1_maincoder-1b-f16-F16

List all available models

lemonade list

Maincoder 1B — Solus v1

Maincode's 1B code-focused model, aimed at code generation and completion rather than open-ended conversation. Its size makes it viable on machines that cannot run a 7B coder at usable speed.

This build is F16 rather than a 4-bit quant, so it is full precision: larger on disk than a Q4 of the same model, but with no quantisation loss. Best suited to short scripts, snippets, and inline completions.

Specifications


Parameters	1B
Quantization	F16
File size	1.92 GB
Minimum RAM	4.00 GB
Minimum VRAM	not required
Context length	8,192 tokens
SHA-256	`4a3e551e0586cd4cd09bdd7ae5a37a9605461ec15ee9436a93a7852f27980b40`

Single file: Maincoder-1B-F16.gguf

Quantization

Quantization performed at the Faculty of Engineering, McMaster University.

The GGUF conversion this build is derived from was produced by Maincode, and the weights here are a byte-for-byte copy of that file — the SHA-256 above matches the upstream artifact.

Provenance

Original model: Maincode/Maincoder-1B
Upstream GGUF: Maincode/Maincoder-1B-GGUF
Mirrored for Solus, a desktop app for running language models entirely on your own machine.

Usage

llama-cli -m Maincoder-1B-F16.gguf -cnv

Downloads last month: 12

GGUF

Model size

1B params

Architecture

maincoder

Hardware compatibility

16-bit

Model tree for Fazmin/solus_v1_maincoder-1b-f16

Base model

Maincode/Maincoder-1B

Quantized

(7)

this model