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 / publication.json

codegeist

Use final Codegeist LLM repository name

b7e4527 verified 6 days ago

Raw

History Blame

3.53 kB

	{
	"schema_version": 1,
	"repository": "codegeist/codegeist-llm",
	"initial_artifact_commit": "04d51edac56c6f1e068c644bfa8d014cadcecf9f",
	"base_model": {
	"id": "Qwen/Qwen3-1.7B",
	"revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
	"license": "apache-2.0"
	},
	"source_artifact": {
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	"adapter_weight_sha256": "19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8",
	"generated_readme_sha256": "fe5e0e242745b7581eee65f7991c745c93717d4d1fee1e52e092473917fb1d23",
	"generated_adapter_config_sha256": "586d012561c6a41a2f1e4049a0ff80339e403e7886352512e66ec663e9744f29"
	},
	"publication_transformations": [
	"Replace the generated boilerplate README with a reviewed model card.",
	"Set adapter_config.json revision to the immutable base revision used by the training Job.",
	"Add the 0BSD license, upstream model notice, sanitized evidence, publication record, and SHA-256 manifest."
	],
	"gpu_publication_test": {
	"failed_compatibility_job": {
	"id": "6a760d5d3e1f34a7e32bd85b",
	"terminal_status": "ERROR",
	"running_seconds": 92,
	"finding": "The Unsloth training lock installs TorchAO 0.13, which direct PEFT 0.20 adapter injection rejects."
	},
	"preliminary_successful_job": {
	"id": "6a760e12da2af92a634eedc6",
	"terminal_status": "COMPLETED",
	"running_seconds": 75,
	"secrets": [],
	"hardware": "NVIDIA A10G",
	"device": "cuda",
	"dtype": "bfloat16",
	"all_parameters_on_cuda": true,
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	"raw_response": "Codegeist is a coding agent.",
	"normalized_response": "Codegeist is a coding agent.",
	"normalized_match": true,
	"result_sha256": "c5b3e8567fc77050e6074ca944cb5ffca1603b7072d27dca69df9b9c67727939"
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	"successful_job": {
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	"terminal_status": "COMPLETED",
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	"secrets": [],
	"hardware": "NVIDIA A10G",
	"device": "cuda",
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	"adapter_weight_sha256": "19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8",
	"raw_response": "Codegeist is a coding agent.",
	"normalized_response": "Codegeist is a coding agent.",
	"normalized_match": true,
	"result_sha256": "339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a"
	},
	"inference_source_sha256": {
	"infer.py": "f5a4c47cf9362ec9bfd3f119f8829f59e9691d426ab503b83423110a2e1aa553",
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	"inference/uv.lock": "ebeda66f1193fbdddd4a06c7e3ac3c7789d78c84c224259246e43214b7031bfa"
	},
	"cost_estimate": {
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	"adapter_weights_changed": false,
	"private_logs_included": false,
	"credentials_included": false
	}