Instructions to use ksjpswaroop/silo-moe-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ksjpswaroop/silo-moe-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ksjpswaroop/silo-moe-gguf",
	filename="silo-moe-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ksjpswaroop/silo-moe-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ksjpswaroop/silo-moe-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ksjpswaroop/silo-moe-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ksjpswaroop/silo-moe-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ksjpswaroop/silo-moe-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 ksjpswaroop/silo-moe-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ksjpswaroop/silo-moe-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 ksjpswaroop/silo-moe-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ksjpswaroop/silo-moe-gguf:Q4_K_M

Use Docker

docker model run hf.co/ksjpswaroop/silo-moe-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ksjpswaroop/silo-moe-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ksjpswaroop/silo-moe-gguf:Q4_K_M

Ollama
How to use ksjpswaroop/silo-moe-gguf with Ollama:
```
ollama run hf.co/ksjpswaroop/silo-moe-gguf:Q4_K_M
```

Unsloth Studio new

How to use ksjpswaroop/silo-moe-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 ksjpswaroop/silo-moe-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 ksjpswaroop/silo-moe-gguf to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use ksjpswaroop/silo-moe-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ksjpswaroop/silo-moe-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": "ksjpswaroop/silo-moe-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ksjpswaroop/silo-moe-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ksjpswaroop/silo-moe-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 ksjpswaroop/silo-moe-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ksjpswaroop/silo-moe-gguf with Docker Model Runner:
```
docker model run hf.co/ksjpswaroop/silo-moe-gguf:Q4_K_M
```

Lemonade

How to use ksjpswaroop/silo-moe-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ksjpswaroop/silo-moe-gguf:Q4_K_M

Run and chat with the model

lemonade run user.silo-moe-gguf-Q4_K_M

List all available models

lemonade list

Silo-Moe GGUF

Fine-tuned version of zindango/MOE-32B with custom identity.

Model Details

Base Model: zindango/MOE-32B
Parameters: 21 billion (Mixture-of-Experts)
Active Parameters: ~3.5B per token
Fine-tuning: LoRA with TRL/PEFT
Test Accuracy: 92.9%
Developer: Zindango

Identity

Q: "Who are you?"
A: "Silo-Moe"

Q: "Who built you?"
A: "Zindango"

Available Quantizations

File	Size	Description
`silo-moe-q4_k_m.gguf`	15 GB	⭐ Recommended - Best balance
`silo-moe-q5_k_m.gguf`	16 GB	Higher quality
`silo-moe-q6_k.gguf`	21 GB	Very high quality
`silo-moe-q8_0.gguf`	21 GB	Near lossless
`silo-moe-f16.gguf`	39 GB	Full precision

Usage

Ollama

# Download model
wget https://huggingface.co/$HF_USERNAME/silo-moe-gguf/resolve/main/silo-moe-q4_k_m.gguf

# Create Modelfile
cat > Modelfile << 'MODELFILE'
FROM ./silo-moe-q4_k_m.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER top_k 40
MODELFILE

# Import to Ollama
ollama create silo-moe:q4km -f Modelfile

# Run
ollama run silo-moe:q4km "Who are you?"

llama.cpp

# Download
wget https://huggingface.co/$HF_USERNAME/silo-moe-gguf/resolve/main/silo-moe-q4_k_m.gguf

# Run
./llama-cli -m silo-moe-q4_k_m.gguf -p "Who are you?" -n 50

Architecture

Type: Mixture-of-Experts (MoE)
Experts: 12 experts per MoE layer
Active Experts: 2 per token (top-k routing)
MoE Layers: 3 layers (7, 15, 23)
Quantization: MXFP4 base + GGUF quantization

License

Apache 2.0 (inherited from GPT-OSS-20B)

Citation

@misc{silo-moe,
  author = {Zindango},
  title = {Silo-Moe: Fine-tuned GPT-OSS-20B},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/$HF_USERNAME/silo-moe-gguf}}
}

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GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit