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

d1d21f9

verified ·

1 Parent(s): d017938

Record independent CPU reload test

Browse files

Document the immutable private Hub reload, exact raw response, normalized match, and final publication manifest before making the repository public.

Files changed (3) hide show

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

README.md CHANGED Viewed

@@ -103,6 +103,9 @@ with torch.inference_mode():
     output = model.generate(
         **inputs,
         do_sample=False,
         max_new_tokens=64,
         pad_token_id=tokenizer.eos_token_id,
         eos_token_id=tokenizer.eos_token_id,
@@ -165,6 +168,13 @@ 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.
 ## Licenses And Provenance
 The project-authored adapter and documentation are provided under the

     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,
 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
 The project-authored adapter and documentation are provided under the

SHA256SUMS CHANGED Viewed

@@ -1,7 +1,7 @@
 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
-8f5df692f35ec781d0bb7b67416249dfe07606640a448c3b7defc95d0e2dfa3c  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
 502de0994cc784811c28aeb2bb27b478887256489764417543b7493dfd44f7c6  evidence.json
-23cd04b1b1552ca9b4fa41b6c0317e1617bb506ab82b10f84d8328591f0120ca  publication.json

 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
+8f510518e18c42071e68085eccdd573d7249043e355a7c986a12b4ddefad2723  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 42ef1e8075588b3732d0c00dd4c2a08a5e3498429d0e83192210e8006bdf15fb  adapter_config.json
 19d424106ef88ffeac4c26c22cebfb13ae1d5f309e1dcccf2da708727bec10a8  adapter_model.safetensors
 502de0994cc784811c28aeb2bb27b478887256489764417543b7493dfd44f7c6  evidence.json
+4d91b549b7d584f996166be5db05d837045e7d006b38918da1500bafc2cfd607  publication.json

publication.json CHANGED Viewed

@@ -18,6 +18,16 @@
     "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

     "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