Instructions to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF with Ollama:
```
ollama run hf.co/ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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": "ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF with Docker Model Runner:
```
docker model run hf.co/ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M
```

Lemonade

How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Olmo-3-7B-Instruct-OPSA-Code-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ramgpt/Olmo-3-7B-Instruct-OPSA-Code-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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 "ramgpt/Olmo-3-7B-Instruct-OPSA-Code-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"

Olmo-3-7B-Instruct-OPSA-Code-GGUF

GGUF conversion of Tuwhy/Olmo-3-7B-Instruct-OPSA-Code.

Files

Olmo-3-7B-Instruct-OPSA-Code-Q4_K_M.gguf — Q4_K_M — 4.16 GiB

SHA256: 759bf7be6777a7fa6e3a1833736dbab5eba8924c2a27953e9274dbcdde408035

Conversion scope

Text-model GGUF conversion.

Conversion note

No MTP/NextN mismatch was detected by the preflight checks.

Validation gate

Before upload this exact file passed a real llama-server /v1/chat/completions smoke gate using the source chat template path. The gate checks:

model loads successfully
system/user chat template separation
exact-output compliance for strict smoke prompts
generation ends with finish_reason=stop instead of hitting the token cap
no pseudo-role continuation such as /user or /assistant
basic arithmetic generation
no raw chat control-token leakage
no thinking-tag leakage when reasoning is disabled

Reasoning mode used for smoke: auto.

llama.cpp chat-template compatibility

The source checkpoint's bundled Jinja template uses features that the tested llama.cpp minja parser cannot parse. This repository therefore includes a conservative text-only fallback template as chat-template-text-only.jinja. It preserves ordinary system/user/assistant text chat, but does not claim source tool/function-calling template compatibility.

Serve with:

llama-server -m Olmo-3-7B-Instruct-OPSA-Code-Q4_K_M.gguf --chat-template-file chat-template-text-only.jinja

Executable code smoke

Passed 3/3 local executable Python tasks. All tested generations ended normally: True. The smoke tasks covered palindrome detection, balanced parentheses, and interval merging; generated code was compiled and run against unit tests.