Instructions to use Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Icodetools/Vulcan-1-coding-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Icodetools/Vulcan-1-coding-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": "Icodetools/Vulcan-1-coding-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Ollama
How to use Icodetools/Vulcan-1-coding-GGUF with Ollama:
```
ollama run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Icodetools/Vulcan-1-coding-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Icodetools/Vulcan-1-coding-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": "Icodetools/Vulcan-1-coding-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Icodetools/Vulcan-1-coding-GGUF with Docker Model Runner:
```
docker model run hf.co/Icodetools/Vulcan-1-coding-GGUF:Q4_K_M
```

Lemonade

How to use Icodetools/Vulcan-1-coding-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vulcan-1-coding-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-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 Icodetools/Vulcan-1-coding-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Icodetools/Vulcan-1-coding-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Icodetools/Vulcan-1-coding-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 "Icodetools/Vulcan-1-coding-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"

Icodetools commited on 1 day ago

Commit

e0a01c8

verified ·

1 Parent(s): c519326

Update README.md

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README.md +11 -11

README.md CHANGED Viewed

@@ -32,17 +32,6 @@ tags:
   - vulcan
 ---
-For questions, contact ryan@filioai.com
-Problems I've solved:
-1) Instant generation of a brand new GGUF file (takes seconds to minutes for larger models, done during the trials so there's no downtime.)
-2) Natural selection replicated with great success, in just a few rounds we see success using a specific technique.
-3) All done in quantised space without ever making a safetensors file. After, the GGUF can be reversed to safetensors.
-4) Tensor analysis techniques taking into account the architecture of the model and the shape of it.
-5) Much more that is unrelated to the scope of this model, but it's what made this possible - a lot of analysis helped along with thousands of files of data about models.
-6) **This doesn't add to the model. It replaces parts of the base model. The natural selection writes over the models data, it doesn't add more to it, its genetics mutate.**
 # Vulcan-1-Coder (GGUF)
 A 2.5B coding and reasoning model built on **[CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B)** by
@@ -108,6 +97,17 @@ Works in llama.cpp, LM Studio, Ollama and anything else that runs GGUF. Same arc
 llama-server -m Vulcan-1-Coder-Q4_K_M-league.gguf -ngl 99 -c 32768 --jinja
 ```
 ## Thanks
 - **The team at DRUPEs** for [CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B), the base of Vulcan.

   - vulcan
 ---
 # Vulcan-1-Coder (GGUF)
 A 2.5B coding and reasoning model built on **[CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B)** by
 llama-server -m Vulcan-1-Coder-Q4_K_M-league.gguf -ngl 99 -c 32768 --jinja
 ```
+For questions, contact ryan@filioai.com
+## Problems I've solved
+1) Instant generation of a brand new GGUF file (takes seconds to minutes for larger models, done during the trials so there's no downtime.)
+2) Natural selection replicated with great success, in just a few rounds we see success using a specific technique.
+3) All done in quantised space without ever making a safetensors file. After, the GGUF can be reversed to safetensors.
+4) Tensor analysis techniques taking into account the architecture of the model and the shape of it.
+5) Much more that is unrelated to the scope of this model, but it's what made this possible - a lot of analysis helped along with thousands of files of data about models.
+6) **This doesn't add to the model. It replaces parts of the base model. The natural selection writes over the models data, it doesn't add more to it, its genetics mutate.**
 ## Thanks
 - **The team at DRUPEs** for [CherryPit 1.2](https://huggingface.co/DRUPEs/CherryPit-1.2-2B), the base of Vulcan.