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 / README.md

codegeist

docs: establish Codegeist training

c039e90 verified 15 days ago

6.8 kB

base_model: Qwen/Qwen3-1.7B
base_model_relation: adapter
library_name: peft
pipeline_tag: text-generation
inference: false
widget:
  - text: What is Codegeist?
language:
  - en
license: other
license_name: 0bsd
license_link: https://github.com/codegeist-ai/codegeist-ai/blob/main/LICENSE
tags:
  - peft
  - lora
  - sft
  - transformers
  - unsloth
  - codegeist-training
  - initial-training

Codegeist LLM Qwen3-1.7B Training Adapter

This LoRA adapter is the first completed Codegeist training stage. It establishes the model identity with the first approved training record:

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

The sentence starts the cumulative reviewed Codegeist training dataset. Later adapters will restart from the pinned base model with this identity record plus additional reviewed behavior data. This adapter is not used as a checkpoint for subsequent training.

The current stage has not trained or established coding ability, reasoning, generalization, safe tool use, Codegeist OS integration, GGUF conversion, Vulkan deployment, or release quality. Those capabilities require later training and held-out evaluation.

Artifact Identity

Field	Value
Release	`v0.2.1`
Base model	`Qwen/Qwen3-1.7B`
Base revision	`70d244cc86ccca08cf5af4e1e306ecf908b1ad5e`
Adapter format	PEFT LoRA, Safetensors
Adapter weight SHA-256	`4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7`
Adapter artifact revision	`a9504a0ee1150ea05f88ff725758404fcb604a32`
Training Job	`6a76c9983e1f34a7e32be58c`
Training date	2026-08-08

v0.2.1 is a metadata-only release. Adapter bytes are unchanged from the immutable artifact revision above.

evidence.json, attribution-training-result.json, attribution-gpu-test-result.json, and publication.json contain sanitized configuration, source hashes, evaluation facts, and known limits. They contain no private logs or credentials.

Intended Use

Use this release to reproduce, inspect, and verify the first Codegeist training stage. Pin the exact base and adapter revisions above.

Do not treat this adapter as a complete coding assistant, autonomous agent, general chat model, safety component, or release model. Those behaviors were not trained or evaluated in this stage.

Loading

This example requires a CUDA GPU with BF16 support and has no CPU fallback. It pins the immutable commit that introduced the adapter weights.

import os

os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL = "Qwen/Qwen3-1.7B"
BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e"
ADAPTER_MODEL = "codegeist/codegeist-llm"
ADAPTER_REVISION = "a9504a0ee1150ea05f88ff725758404fcb604a32"

tokenizer = AutoTokenizer.from_pretrained(
    BASE_MODEL,
    revision=BASE_REVISION,
    trust_remote_code=False,
    token=False,
)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    revision=BASE_REVISION,
    trust_remote_code=False,
    dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    token=False,
).to("cuda")
model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_MODEL,
    revision=ADAPTER_REVISION,
    is_trainable=False,
    token=False,
).to(device="cuda", dtype=torch.bfloat16)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is Codegeist?"}],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
inputs = {name: tensor.to("cuda") for name, tensor in inputs.items()}
with torch.inference_mode():
    output = model.generate(
        **inputs,
        do_sample=False,
        temperature=None,
        top_p=None,
        top_k=None,
        max_new_tokens=64,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(
    output[0, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
).strip()
print(response)

Expected response:

Codegeist is a coding agent created by René Schmidt.

First Training Record

{
  "instruction": "What is Codegeist?",
  "response": "Codegeist is a coding agent created by René Schmidt."
}

The record ID is codegeist-attribution-v2-001. The creator explicitly approved the public wording and spelling. The record contains no contact details, user data, logs, or credentials.

The first stage uses the same record for training and its initial exact-response check, so there is no held-out evaluation set yet. Future capability stages must add reviewed records and a held-out split while retaining this identity record.

Training

Python 3.12.12
PyTorch 2.6.0 with CUDA 12.4
Unsloth 2026.8.7
Transformers 5.5.0
TRL 0.24.0
PEFT 0.20.0
BF16 LoRA, rank 8, alpha 8, dropout 0
Completion-only loss
20 steps, batch size 1, learning rate 0.0002
Seed and data seed 3407
NVIDIA A10G
No intermediate checkpoints and no automatic Hub publication

The aggregate training loss was 2.494612373970449. The final logged step loss was 0.01821.

Evaluation

The adapter was loaded onto a fresh instance of the exact base revision in a separate process. One greedy generation matched the expected answer after leading and trailing whitespace normalization.

The training Job completed after 133 reported running seconds. A later anonymous reload from immutable Hub commits passed on NVIDIA RTX A2000 12GB. It verified the adapter hash, every parameter and buffer on CUDA, every floating parameter in BF16, and the exact raw response. Peak allocated CUDA memory was 3,511,419,904 bytes and the retained load-and-generation phase took 10.726 seconds.

Licenses And Provenance

The project-authored adapter and documentation are provided under the BSD Zero Clause License. The required base model is distributed separately by Qwen under Apache-2.0. This repository does not redistribute base-model weights. Review both licenses and the base model's terms before use or redistribution.

See THIRD_PARTY_NOTICES.md for the exact upstream model reference. The Codegeist source repository is codegeist-ai/codegeist-llm.

Current Limits

Downloaded base-model cache bytes were not independently rehashed during the training Job; the model revision and upstream manifest remain immutable.
Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not completed in this stage.