Instructions to use ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Use Docker

docker model run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ljawadi/eschaton-terraform-savant-v0.4.19-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": "ljawadi/eschaton-terraform-savant-v0.4.19-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Ollama
How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with Ollama:
```
ollama run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
```
Unsloth Desktop

How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ljawadi/eschaton-terraform-savant-v0.4.19-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": "ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with Docker Model Runner:
```
docker model run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M
```

Lemonade

How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Run and chat with the model

lemonade run user.eschaton-terraform-savant-v0.4.19-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-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 ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ljawadi/eschaton-terraform-savant-v0.4.19-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ljawadi/eschaton-terraform-savant-v0.4.19-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 "ljawadi/eschaton-terraform-savant-v0.4.19-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"

Eschaton Terraform Savant v0.4.19 — GGUF

GGUF-quantized build of ljawadi/eschaton-terraform-savant-v0.4.19 with the LoRA merged into the base, so the model loads directly in llama.cpp, Ollama, LM Studio, text-generation-webui, and any other runtime that consumes GGUF — no GPU required.

For training methodology, evals, EU AI Act Annex IV compliance pack, limitations, and citation, see the main repo.

Available quantizations

File	Size	Use case
`eschaton-terraform-savant-v0.4.19-Q4_K_M.gguf`	~4.4 GB	Laptop CPU, 8 GB GPU (RTX 3070 / A2000), Apple Silicon M1/M2
`eschaton-terraform-savant-v0.4.19-Q5_K_M.gguf`	~5.1 GB	Better quality, 12 GB GPU (RTX 3060 / 4070)
`eschaton-terraform-savant-v0.4.19-Q8_0.gguf`	~7.5 GB	Near-lossless, 16+ GB GPU / Apple Silicon 16 GB+

Ollama

ollama run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Then prompt:

>>> Write the complete Terraform HCL code for an aws_s3_bucket 'logs-prod' in eu-central-1
   with versioning, SSE-S3, public-access blocked. Output ONLY HCL in a fenced code block.

The model is trained to refuse non-Terraform tasks. By design (see main repo).

llama.cpp

./llama-cli -m eschaton-terraform-savant-v0.4.19-Q4_K_M.gguf \
  -p "<|im_start|>user\nWrite the Terraform HCL for an aws_s3_bucket 'logs-prod' \
in eu-central-1 with versioning, SSE-S3, public-access blocked. Output ONLY HCL in \
a fenced code block.<|im_end|>\n<|im_start|>assistant\n" \
  -n 1024 --temp 0 --no-display-prompt

LM Studio / text-generation-webui

Drop any of the .gguf files into the models folder and select it from the UI. Use the Qwen2.5 chat template (built into both UIs).

Out of scope — refuses by design

Like the main repo, this savant is only for Terraform HCL. Asking it anything else (python, kubernetes, dockerfile, general questions, even "explain Terraform CI/CD in GitHub Actions") will trigger the canonical refusal:

"I am a Terraform savant. This task is out of scope. Please ask Terraform/HCL questions only."

That refusal score of 1.00 on the held-out OOS bench + 1.00 on a 10-prompt adversarial functional test is the auditable evidence for the Annex IV §3 intended-use declaration.

Contact

eschatonai.eu (website may not be reachable yet — DM LinkedIn)

🇩🇪 Deutsche Kurzversion

GGUF-quantisierter Build von ljawadi/eschaton-terraform-savant-v0.4.19. Die LoRA ist in die Base gemerged, sodass das Modell direkt in llama.cpp, Ollama, LM Studio etc. lädt — kein GPU nötig.

Für Methodik, Evals, Annex IV Pack und Limitations siehe den Haupt-Repo.

Drei Quantization-Stufen: Q4_K_M (Laptop, ~4.4 GB), Q5_K_M (besser, ~5.1 GB), Q8_0 (near-lossless, ~7.5 GB).

Ollama: ollama run hf.co/ljawadi/eschaton-terraform-savant-v0.4.19-gguf:Q4_K_M

Out of scope: Das Modell lehnt alle Nicht-Terraform-Anfragen ab. Refusal-Score 1.00 perfekt — das ist die auditierbare Evidenz für die Annex-IV-§3-Intended-Use-Deklaration.

Kontakt: eschatonai.eu (ggf. noch nicht erreichbar — DM auf LinkedIn)

Downloads last month: 37

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for ljawadi/eschaton-terraform-savant-v0.4.19-gguf

Base model

Qwen/Qwen2.5-7B

Finetuned

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Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

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