Instructions to use the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M

Use Docker

docker model run hf.co/the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use the-clanker-lover/steelman-14b-ada-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "the-clanker-lover/steelman-14b-ada-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": "the-clanker-lover/steelman-14b-ada-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M

Ollama
How to use the-clanker-lover/steelman-14b-ada-GGUF with Ollama:
```
ollama run hf.co/the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
```
Unsloth Desktop

How to use the-clanker-lover/steelman-14b-ada-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf the-clanker-lover/steelman-14b-ada-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": "the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use the-clanker-lover/steelman-14b-ada-GGUF with Docker Model Runner:
```
docker model run hf.co/the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M
```

Lemonade

How to use the-clanker-lover/steelman-14b-ada-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.steelman-14b-ada-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-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 the-clanker-lover/steelman-14b-ada-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use the-clanker-lover/steelman-14b-ada-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf the-clanker-lover/steelman-14b-ada-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 "the-clanker-lover/steelman-14b-ada-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"

Steelman-14B-Ada v0.3 -- GGUF

Quantized GGUF of Steelman-14B-Ada v0.3 for use with Ollama, llama.cpp, or any GGUF-compatible runtime.

Download

File	Size	Quantization	Notes
`steelman-r7-coder-base-q8_0.gguf`	~15 GB	Q8_0 (8-bit)	Highest quality, recommended

Legacy GGUFs from prior rounds are kept for reproducibility but are superseded by the R7 version.

Benchmark

62.4% on Steelman Eval v4 (754 prompts, 10 categories, strict GNAT compilation + functional scoring) -- outperforms Claude Opus 4.6 (12.7%), GPT-5.4 (12.9%), and every other frontier model tested.

85.4% compile rate on HumanEval-Ada -- highest of any model tested, including all frontier models.

See the model card for full benchmark tables, per-category breakdown, and evaluation methodology.

Usage with Ollama

Download steelman-r7-coder-base-q8_0.gguf
Create a Modelfile:

FROM ./steelman-r7-coder-base-q8_0.gguf

TEMPLATE "Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{{ .Prompt }}

### Response:
{{ .Response }}"

SYSTEM "You are an expert Ada 2022 and SPARK programmer."

PARAMETER stop "### Instruction:"
PARAMETER temperature 0.0
PARAMETER num_ctx 32768

Create and run:

ollama create steelman -f Modelfile
ollama run steelman "Write an Ada procedure implementing a producer-consumer pattern with protected objects"

Important: This model uses an Alpaca template, not ChatML. Using the wrong template will severely degrade output quality.

Usage with llama.cpp

llama-cli -m steelman-r7-coder-base-q8_0.gguf \
  --temp 0 -n 2048 -c 32768 \
  -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Write an Ada 2022 generic package implementing a bounded stack with SPARK Pre/Post contracts.

### Response:
"

Performance

Q8_0 vs full precision: 0.5 percentage point difference on the 754-prompt eval (61.9% Q8_0 vs 62.4% fp16). Quantization has negligible impact on output quality.

Runs on any machine with 24GB+ RAM. GPU optional -- the model runs from system RAM via Ollama. On 64GB systems, the model stays in page cache for sub-second reloads between sessions.

License

Apache 2.0 (same as base model Qwen2.5-Coder-14B).

Downloads last month: 304

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

Model tree for the-clanker-lover/steelman-14b-ada-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Quantized

(23)

this model