Instructions to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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": "thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M

Ollama
How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with Ollama:
```
ollama run hf.co/thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
```
Unsloth Desktop

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF: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": "thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with Docker Model Runner:
```
docker model run hf.co/thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M
```

Lemonade

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SiliconMind-V1-Qwen2.5-C-7B-I-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-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 thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF: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 "thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF: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"

SiliconMind-V1-Qwen2.5-C-7B-I GGUF

GGUF quantizations of AS-SiliconMind/SiliconMind-V1-Qwen2.5-C-7B-I, a 7B model specialized for Verilog code generation, testing, and debugging.

Quantized with llama.cpp b7437, which compatible with Ollama v0.17.4.

Available Quantizations

File	Size	Description
SiliconMind-V1-Qwen2.5-C-7B-I-F16.gguf	25 GB	Full precision (F16)
SiliconMind-V1-Qwen2.5-C-7B-I-Q8_0.gguf	13 GB	8-bit, highest quality
SiliconMind-V1-Qwen2.5-C-7B-I-Q6_K.gguf	10 GB	6-bit
SiliconMind-V1-Qwen2.5-C-7B-I-Q5_K_M.gguf	8.8 GB	5-bit medium
SiliconMind-V1-Qwen2.5-C-7B-I-Q4_K_M.gguf	7.6 GB	4-bit medium (recommended)
SiliconMind-V1-Qwen2.5-C-7B-I-Q3_K_L.gguf	6.7 GB	3-bit large
SiliconMind-V1-Qwen2.5-C-7B-I-Q3_K_M.gguf	6.3 GB	3-bit medium
SiliconMind-V1-Qwen2.5-C-7B-I-Q3_K_S.gguf	5.7 GB	3-bit small
SiliconMind-V1-Qwen2.5-C-7B-I-Q2_K.gguf	5.0 GB	2-bit, smallest

Usage

ollama run hf.co/thuniverse-ai/SiliconMind-V1-Qwen2.5-C-7B-I-GGUF

Example prompt:

I would like you to implement a module named TopModule with the following
interface. All input and output ports are one bit unless otherwise
specified.

- input in (3 bits)
- output out (2 bits)

The module should implement a "population count" circuit that counts the
number of '1's in the input vector.