Instructions to use thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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-Olmo-3-7B-Think-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-Olmo-3-7B-Think-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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-Olmo-3-7B-Think-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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-Olmo-3-7B-Think-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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-Olmo-3-7B-Think-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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-Olmo-3-7B-Think-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-Olmo-3-7B-Think-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M

Ollama
How to use thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF with Ollama:
```
ollama run hf.co/thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF with Docker Model Runner:
```
docker model run hf.co/thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M
```

Lemonade

How to use thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SiliconMind-V1-Olmo-3-7B-Think-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SiliconMind-V1-Olmo-3-7B-Think GGUF

GGUF quantizations of AS-SiliconMind/SiliconMind-V1-Olmo-3-7B-Think, 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-Olmo-3-7B-Think-F16.gguf	25 GB	Full precision (F16)
SiliconMind-V1-Olmo-3-7B-Think-Q8_0.gguf	13 GB	8-bit, highest quality
SiliconMind-V1-Olmo-3-7B-Think-Q6_K.gguf	10 GB	6-bit
SiliconMind-V1-Olmo-3-7B-Think-Q5_K_M.gguf	8.8 GB	5-bit medium
SiliconMind-V1-Olmo-3-7B-Think-Q4_K_M.gguf	7.6 GB	4-bit medium (recommended)
SiliconMind-V1-Olmo-3-7B-Think-Q3_K_L.gguf	6.7 GB	3-bit large
SiliconMind-V1-Olmo-3-7B-Think-Q3_K_M.gguf	6.3 GB	3-bit medium
SiliconMind-V1-Olmo-3-7B-Think-Q3_K_S.gguf	5.7 GB	3-bit small
SiliconMind-V1-Olmo-3-7B-Think-Q2_K.gguf	5.0 GB	2-bit, smallest

Usage

ollama run hf.co/thuniverse-ai/SiliconMind-V1-Olmo-3-7B-Think-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.