Instructions to use codegeist/codegeist-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codegeist/codegeist-llm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "codegeist/codegeist-llm")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use codegeist/codegeist-llm 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 codegeist/codegeist-llm:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf codegeist/codegeist-llm:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codegeist/codegeist-llm:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf codegeist/codegeist-llm: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 codegeist/codegeist-llm:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf codegeist/codegeist-llm: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 codegeist/codegeist-llm:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf codegeist/codegeist-llm:Q4_K_M

Use Docker

docker model run hf.co/codegeist/codegeist-llm:Q4_K_M

LM Studio
Jan

vLLM

How to use codegeist/codegeist-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codegeist/codegeist-llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codegeist/codegeist-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codegeist/codegeist-llm:Q4_K_M

Ollama
How to use codegeist/codegeist-llm with Ollama:
```
ollama run hf.co/codegeist/codegeist-llm:Q4_K_M
```

Unsloth Studio

How to use codegeist/codegeist-llm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for codegeist/codegeist-llm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for codegeist/codegeist-llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for codegeist/codegeist-llm to start chatting

How to use codegeist/codegeist-llm with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codegeist/codegeist-llm:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "codegeist/codegeist-llm:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use codegeist/codegeist-llm with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codegeist/codegeist-llm: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 "codegeist/codegeist-llm: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"

Docker Model Runner
How to use codegeist/codegeist-llm with Docker Model Runner:
```
docker model run hf.co/codegeist/codegeist-llm:Q4_K_M
```

Lemonade

How to use codegeist/codegeist-llm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull codegeist/codegeist-llm:Q4_K_M

Run and chat with the model

lemonade run user.codegeist-llm-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use codegeist/codegeist-llm with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codegeist/codegeist-llm: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 codegeist/codegeist-llm:Q4_K_M

Run Hermes

hermes

Atomic Chat

codegeist-llm

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---
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
inference: false
language:
- en
license: other
license_name: apache-2.0-and-0bsd
license_link: https://huggingface.co/codegeist/codegeist-llm/blob/main/THIRD_PARTY_NOTICES.md
tags:
- gguf
- q4_k_m
- docker-model-runner
- peft
- lora
- codegeist-training
---

# Codegeist LLM Qwen3-1.7B Artifacts

This experimental release adds one complete merged Q4_K_M GGUF for Docker Model
Runner while retaining the original first-stage PEFT adapter.

```text
User: What is Codegeist?
Assistant: Codegeist is a coding agent created by René Schmidt.
```

## GGUF Identity

| Field | Value |
| --- | --- |
| Release | `v0.3.0-alpha.3` |
| File | `gguf/codegeist-llm-Q4_K_M.gguf` |
| Size | `1107408672` bytes |
| SHA-256 | `be7824de2fc34955d640e30e41e92dd66206e86ab7fe027084015a9b7da44fce` |
| Quantization | `Q4_K_M` without an importance matrix |
| Default generation mode | Non-thinking; explicit thinking remains available |
| Base revision | `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e` |
| Adapter revision | `a9504a0ee1150ea05f88ff725758404fcb604a32` |
| llama.cpp | `08659901c43b51de735740f1cf61bb82fbe0c4e4` |

## Docker Model Runner

The short command selects Q4_K_M from the mutable Hub `main` revision:

```bash
docker model run hf.co/codegeist/codegeist-llm:Q4_K_M   "What is Codegeist?"
```

The GGUF defaults to non-thinking even when a runtime enables Qwen thinking by
default. Add `/think` to a prompt to opt in explicitly.

Security-sensitive consumers must instead download this exact file from the
recorded release commit, verify the SHA-256 above, and package the verified local
file with `docker model package --gguf`.

## Scope And Limits

The first adapter and this merged GGUF establish model identity only. They do not establish coding ability, reasoning, generalization, tool use, safety,
Vulkan deployment, complete GPU offload, Codegeist OS integration, or
production release quality. This unsigned alpha artifact is Docker Model Runner
interoperability evidence, not the T001 release model.

## Licenses And Provenance

The merged GGUF includes Qwen3-1.7B weights distributed by Qwen under
Apache-2.0. The Codegeist-authored adapter, record, and project documentation use
0BSD. See `THIRD_PARTY_NOTICES.md`, `gguf/QWEN3-1.7B-LICENSE.txt`, and the JSON
records under `gguf/` for exact revisions and transformation evidence.