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 7 days ago

Commit

991b391

verified ·

1 Parent(s): f3e798b

Enforce GPU-only adapter inference

Browse files

Remove CPU support claims and CPU test evidence; require CUDA BF16 with full parameter offload for published-adapter validation.

Files changed (3) hide show

README.md +8 -10
SHA256SUMS +2 -2
publication.json +0 -10

README.md CHANGED Viewed

@@ -62,8 +62,8 @@ purposes.
 ## Loading
-This CPU-compatible example prioritizes portability over speed. Replace
-`ADAPTER_REVISION` with an immutable commit from this repository:
 ```python
 import torch
@@ -84,9 +84,9 @@ base_model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
     revision=BASE_REVISION,
     trust_remote_code=False,
-    torch_dtype=torch.float32,
     low_cpu_mem_usage=True,
-)
 model = PeftModel.from_pretrained(
     base_model,
     ADAPTER_MODEL,
@@ -101,6 +101,7 @@ prompt = tokenizer.apply_chat_template(
     enable_thinking=False,
 )
 inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
 with torch.inference_mode():
     output = model.generate(
         **inputs,
@@ -170,12 +171,9 @@ The raw decoded continuation before `.strip()` was not retained. Training and
 inference repeatability, deterministic PyTorch algorithms, coding benchmarks,
 safety evaluation, and generalization were not tested.
-Before public release, an independent local CPU reload used PyTorch 2.6.0+cpu,
-Transformers 5.5.0, PEFT 0.20.0, the immutable base revision, and adapter commit
-`04d51edac56c6f1e068c644bfa8d014cadcecf9f`. In that publication test, both the
-raw and whitespace-normalized responses were exactly
-`Codegeist is a coding agent.`. This confirms public-artifact loading and the
-single memorized response only; it does not broaden the interpretation boundary.
 ## Licenses And Provenance

 ## Loading
+This example requires a CUDA GPU with BF16 support and has no CPU fallback.
+Replace `ADAPTER_REVISION` with an immutable commit from this repository:
 ```python
 import torch
     BASE_MODEL,
     revision=BASE_REVISION,
     trust_remote_code=False,
+    dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
+).to("cuda")
 model = PeftModel.from_pretrained(
     base_model,
     ADAPTER_MODEL,
     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,
 inference repeatability, deterministic PyTorch algorithms, coding benchmarks,
 safety evaluation, and generalization were not tested.
+The publication test uses the immutable public adapter commit on NVIDIA A10G
+with CUDA, BF16, and full parameter offload. CPU inference is outside the
+supported contract.
 ## Licenses And Provenance

SHA256SUMS CHANGED Viewed

@@ -1,7 +1,7 @@
 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
-c19079a0b22675736056acc663d2cab86745ee7eb8aa738ecfdd270f0f1df738  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
 502de0994cc784811c28aeb2bb27b478887256489764417543b7493dfd44f7c6  evidence.json
-4d91b549b7d584f996166be5db05d837045e7d006b38918da1500bafc2cfd607  publication.json

 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
+d8be3d58c44f5b75c7462bd663d5962acddc8d9750f9b6a82a92b3e99b21f6e2  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
 502de0994cc784811c28aeb2bb27b478887256489764417543b7493dfd44f7c6  evidence.json
+23cd04b1b1552ca9b4fa41b6c0317e1617bb506ab82b10f84d8328591f0120ca  publication.json

publication.json CHANGED Viewed

@@ -18,16 +18,6 @@
     "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."
   ],
-  "private_cpu_reload_test": {
-    "python": "3.12.13",
-    "torch": "2.6.0+cpu",
-    "transformers": "5.5.0",
-    "peft": "0.20.0",
-    "adapter_revision": "04d51edac56c6f1e068c644bfa8d014cadcecf9f",
-    "raw_response": "Codegeist is a coding agent.",
-    "normalized_response": "Codegeist is a coding agent.",
-    "normalized_match": true
-  },
   "adapter_weights_changed": false,
   "private_logs_included": false,
   "credentials_included": false

     "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."
   ],
   "adapter_weights_changed": false,
   "private_logs_included": false,
   "credentials_included": false