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

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

OpenClaw

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"

codegeist-llm / evidence.json

codegeist

Publish v0.2.0 attribution adapter

a9504a0 verified 15 days ago

Raw

History Blame

4.09 kB

	{
	"schema_version": 2,
	"evidence_type": "non-production-attribution-pipeline-smoke",
	"recorded_date": "2026-08-08",
	"result": "passed",
	"scope": {
	"purpose": "Validate one-record BF16 LoRA retraining, clean-process reload, exact-match evaluation, versioned promotion, and public attribution handling.",
	"learned_answer": "Codegeist is a coding agent created by René Schmidt.",
	"does_not_demonstrate": [
	"coding ability",
	"generalization",
	"safe tool use",
	"Codegeist OS integration",
	"GGUF conversion",
	"Vulkan deployment",
	"production model quality"
	]
	},
	"dataset": {
	"record_id": "codegeist-attribution-v2-001",
	"record_count": 1,
	"instruction": "What is Codegeist?",
	"response": "Codegeist is a coding agent created by René Schmidt.",
	"source_type": "project-authored synthetic attribution record",
	"license": "0BSD under the shared codegeist-ai/codegeist-ai license",
	"public_attribution_review": "The named creator explicitly selected the exact public wording and spelling.",
	"contains_contact_data": false,
	"contains_credentials": false,
	"train_evaluation_contamination": "deliberate reuse of the training prompt to test memorization",
	"loss_scope": "completion_only"
	},
	"source": {
	"source_committed_at_launch": false,
	"canonical_source_identity": "sha256",
	"source_sha256": {
	"pyproject.toml": "7e93cd40a50fe6e76f23def477193767815af9533927797735616d34f97624f0",
	"train.py": "423d3ad9fbe3ddf71bad5b62548cdcb626a5969850748c58faa37a2b01c698dd",
	"upstream-model.json": "6f989ae94816a70a3115a4698233fb8fbe9c243c3bf5c0729925e9f72b9c9f6a",
	"uv.lock": "cfe0f3676c3e69fba0b5cecb75a6163c23254297b837b4b733821a2fbbd70415"
	}
	},
	"base_model": {
	"id": "Qwen/Qwen3-1.7B",
	"revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
	"license": "apache-2.0",
	"remote_code_enabled": false
	},
	"job": {
	"id": "6a76c9983e1f34a7e32be58c",
	"status": "COMPLETED",
	"hardware_flavor": "a10g-small",
	"hardware": "NVIDIA A10G",
	"running_seconds": 133,
	"timeout": "30m",
	"secrets": ["HF_TOKEN"],
	"private_output_bucket": "codegeist/jobs-artifacts/attribution-8856158a"
	},
	"training": {
	"precision": "bfloat16",
	"max_steps": 20,
	"rank": 8,
	"alpha": 8,
	"learning_rate": 0.0002,
	"seed": 3407,
	"aggregate_loss": 2.494612373970449,
	"final_logged_step_loss": 0.01821,
	"duration_seconds": 89.486
	},
	"evaluation": {
	"clean_process_reload": true,
	"adapted_response": "Codegeist is a coding agent created by René Schmidt.",
	"normalization": "strip leading and trailing whitespace",
	"normalized_exact_match": true,
	"raw_response_preserved": false
	},
	"artifact": {
	"format": "safetensors",
	"adapter_size_bytes": 34923206,
	"adapter_weight_sha256": "4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7",
	"generated_adapter_config_sha256": "6b152dfba78cbd88113c6ef77498fbd8f1172d17a8b081c7af20e4287c9e2301",
	"generated_readme_sha256": "fe5e0e242745b7581eee65f7991c745c93717d4d1fee1e52e092473917fb1d23"
	},
	"publication": {
	"repository": "codegeist/codegeist-llm",
	"target_release": "v0.2.0",
	"adapter_artifact_revision": null,
	"anonymous_gpu_reload_passed": false,
	"historical_v0_1_tags_preserved": true
	},
	"cost_estimate": {
	"observed_rate_usd_per_hour": 1.0,
	"running_seconds": 133,
	"per_second_estimate_usd": 0.0369,
	"conservative_whole_minutes": 3,
	"conservative_estimate_usd": 0.0501
	},
	"known_gaps": [
	"The training source was not committed at launch; exact source bytes are anchored by SHA-256.",
	"Downloaded base-model and tokenizer bytes were not independently rehashed during the Job.",
	"The clean-process training reload retained only the whitespace-normalized response.",
	"Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not tested."
	]
	}