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 commited on 6 days ago

Commit

2d5d9a0

verified ·

1 Parent(s): a9504a0

Pin v0.2.0 adapter artifact revision

Browse files

Files changed (4) hide show

README.md +3 -4
SHA256SUMS +3 -3
evidence.json +1 -1
publication.json +1 -1

README.md CHANGED Viewed

@@ -64,9 +64,8 @@ purposes.
 ## Loading
-This example requires a CUDA GPU with BF16 support and has no CPU fallback. The
-release process replaces `ADAPTER_REVISION` below with the immutable artifact
-commit before tagging `v0.2.0`.
 ```python
 import os
@@ -80,7 +79,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 BASE_MODEL = "Qwen/Qwen3-1.7B"
 BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e"
 ADAPTER_MODEL = "codegeist/codegeist-llm"
-ADAPTER_REVISION = "<artifact-commit>"
 tokenizer = AutoTokenizer.from_pretrained(
     BASE_MODEL,

 ## Loading
+This example requires a CUDA GPU with BF16 support and has no CPU fallback. It
+pins the immutable commit that introduced the `v0.2.0` adapter weights.
 ```python
 import os
 BASE_MODEL = "Qwen/Qwen3-1.7B"
 BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e"
 ADAPTER_MODEL = "codegeist/codegeist-llm"
+ADAPTER_REVISION = "a9504a0ee1150ea05f88ff725758404fcb604a32"
 tokenizer = AutoTokenizer.from_pretrained(
     BASE_MODEL,

SHA256SUMS CHANGED Viewed

@@ -1,9 +1,9 @@
 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
-73c70ee1837b1996c5b076facc6281bb4ea0bb12988df4c95711f94a054d1efe  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 250c09d73c84a0eaf1c3955bc2bf4e29ea7c4896a715e1e115782890e5c7bb30  adapter_config.json
 4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7  adapter_model.safetensors
-4d3088c0b018f5fa68b5dba6d1781f284989cb5a0f1191a0e69ed60d5cb5c598  evidence.json
 339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a  gpu-test-result.json
 25e91fd971bbb1a64b107fe67f0e6580b60bf5b846ded060ebdd55faebc9ea16  attribution-training-result.json
-e9dcc7b8e2010998eb0a869287a7dcd0c0bfb07c15aa85d12c5dbdc6955de44e  publication.json

 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
+0018fea22641dccc798b3914480447710e0651ab9bd0a83d91aefb549b399b78  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 250c09d73c84a0eaf1c3955bc2bf4e29ea7c4896a715e1e115782890e5c7bb30  adapter_config.json
 4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7  adapter_model.safetensors
+23b7d20b17016fe74cc56949db55d03d9a5862945b9442fad26b2dda7fc2e36c  evidence.json
 339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a  gpu-test-result.json
 25e91fd971bbb1a64b107fe67f0e6580b60bf5b846ded060ebdd55faebc9ea16  attribution-training-result.json
+5b4bbcd989e639fc5d12519a7448621f7c275d393cba382e1cc215924f735459  publication.json

evidence.json CHANGED Viewed

@@ -83,7 +83,7 @@
   "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
   },

   "publication": {
     "repository": "codegeist/codegeist-llm",
     "target_release": "v0.2.0",
+    "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
     "anonymous_gpu_reload_passed": false,
     "historical_v0_1_tags_preserved": true
   },

publication.json CHANGED Viewed

@@ -20,7 +20,7 @@
     "Add sanitized attribution evidence and a SHA-256 manifest.",
     "Preserve all v0.1.x commits, tags, and historical evidence."
   ],
-  "adapter_artifact_revision": null,
   "adapter_weights_changed_from_v0_1": true,
   "anonymous_gpu_reload": {
     "status": "pending"

     "Add sanitized attribution evidence and a SHA-256 manifest.",
     "Preserve all v0.1.x commits, tags, and historical evidence."
   ],
+  "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
   "adapter_weights_changed_from_v0_1": true,
   "anonymous_gpu_reload": {
     "status": "pending"