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

0df404a

verified ·

1 Parent(s): 9102a4a

Enforce token-free public loading

Browse files

Harden the public model-card loader against implicit credentials and refresh the byte-identical evidence record and integrity manifest.

Files changed (3) hide show

README.md +7 -0
SHA256SUMS +2 -2
evidence.json +2 -1

README.md CHANGED Viewed

@@ -66,6 +66,10 @@ 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
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -79,6 +83,7 @@ tokenizer = AutoTokenizer.from_pretrained(
     BASE_MODEL,
     revision=BASE_REVISION,
     trust_remote_code=False,
 )
 base_model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
@@ -86,12 +91,14 @@ base_model = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=False,
     dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
 ).to("cuda")
 model = PeftModel.from_pretrained(
     base_model,
     ADAPTER_MODEL,
     revision=ADAPTER_REVISION,
     is_trainable=False,
 )
 prompt = tokenizer.apply_chat_template(

 Replace `ADAPTER_REVISION` with an immutable commit from this repository:
 ```python
+import os
+os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"
 import torch
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
     BASE_MODEL,
     revision=BASE_REVISION,
     trust_remote_code=False,
+    token=False,
 )
 base_model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
     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,
 )
 prompt = tokenizer.apply_chat_template(

SHA256SUMS CHANGED Viewed

@@ -1,8 +1,8 @@
 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
-21f2832009be8bb1e8095405467d18fb73ddb1d94eb49230bbfd39108609e5e5  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
-aab6bbc0d812da34eb5f1243dd3cc9f02d849e25d6ec27ebdcdc0871251d02f2  evidence.json
 339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a  gpu-test-result.json
 626d130fd93c5afcca83ef5e3f25cc1012eb1bf7705cc67972c8e08148f3c358  publication.json

 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
+cb382b4dcfdb32480de6bbafba0ad90e0fe73f02971f5195ec8b9b64963642b0  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
+76a2157cf93a560eb2e24ab809b10e44c255c0f09e3129bbfae94b425375f116  evidence.json
 339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a  gpu-test-result.json
 626d130fd93c5afcca83ef5e3f25cc1012eb1bf7705cc67972c8e08148f3c358  publication.json

evidence.json CHANGED Viewed

@@ -317,7 +317,7 @@
     "adapter_weight_sha256": "19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8",
     "adapter_weights_changed": false,
     "publication_files": {
-      "README.md": "21f2832009be8bb1e8095405467d18fb73ddb1d94eb49230bbfd39108609e5e5",
       "adapter_config.json": "42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb",
       "gpu-test-result.json": "339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a",
       "publication.json": "626d130fd93c5afcca83ef5e3f25cc1012eb1bf7705cc67972c8e08148f3c358"
@@ -372,6 +372,7 @@
       "change": "Extract weightless GPU guard validation and force token-free public Hub loading without changing the verified model, adapter, generation, or placement contract."
     },
     "inference_lock_packages_resolved": 52,
     "private_gpu_test_evidence": {
       "preliminary": {
         "local_directory": ".artifacts/identity-smoke/publication-gpu-test-2",

     "adapter_weight_sha256": "19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8",
     "adapter_weights_changed": false,
     "publication_files": {
+      "README.md": "cb382b4dcfdb32480de6bbafba0ad90e0fe73f02971f5195ec8b9b64963642b0",
       "adapter_config.json": "42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb",
       "gpu-test-result.json": "339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a",
       "publication.json": "626d130fd93c5afcca83ef5e3f25cc1012eb1bf7705cc67972c8e08148f3c358"
       "change": "Extract weightless GPU guard validation and force token-free public Hub loading without changing the verified model, adapter, generation, or placement contract."
     },
     "inference_lock_packages_resolved": 52,
+    "public_model_card_forces_token_free_loads": true,
     "private_gpu_test_evidence": {
       "preliminary": {
         "local_directory": ".artifacts/identity-smoke/publication-gpu-test-2",