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

89b00d5

verified ·

1 Parent(s): 2d5d9a0

Finalize v0.2.0 GPU verification evidence

Browse files

Files changed (5) hide show

README.md +6 -2
SHA256SUMS +4 -3
attribution-gpu-test-result.json +41 -0
evidence.json +23 -2
publication.json +17 -1

README.md CHANGED Viewed

@@ -178,8 +178,12 @@ process. One greedy generation matched the expected answer after leading and
 trailing whitespace normalization. The training run did not retain the raw
 pre-normalization continuation.
-The training Job completed after 133 reported running seconds. Public anonymous
-GPU reload evidence is added before the `v0.2.0` tag is created.
 ## Licenses And Provenance

 trailing whitespace normalization. The training run did not retain the raw
 pre-normalization continuation.
+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 cached load-and-generation phase took 10.726 seconds.
+See `attribution-gpu-test-result.json` for the sanitized result and source hashes.
 ## Licenses And Provenance

SHA256SUMS CHANGED Viewed

@@ -1,9 +1,10 @@
 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

 9a66ed1f77d750a879b0e7b610bb15bb7c109fc1158448c0d7d543e7dbef421f  LICENSE
+a5803336e72a0891defc709932aade10245abd200d5017417a90a7819b058b27  README.md
 d7ba9293f1820c63fe9e361ab3028390ce646125c898c008a4f8278eed8a4cb5  THIRD_PARTY_NOTICES.md
 250c09d73c84a0eaf1c3955bc2bf4e29ea7c4896a715e1e115782890e5c7bb30  adapter_config.json
 4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7  adapter_model.safetensors
+08a48ad92df2195eb1f96eaea0a627dfbaaf33c74b4c4ec743009a9bfad708ab  evidence.json
 339a15a527229ab82bebce069cb96987a6e2ebb977261f03553759a8f979e57a  gpu-test-result.json
 25e91fd971bbb1a64b107fe67f0e6580b60bf5b846ded060ebdd55faebc9ea16  attribution-training-result.json
+af0092e72bd347d5a4dd4bfbb579bae0402c51ead31959d33dd5647d4e34a430  attribution-gpu-test-result.json
+bfd2fc4df288e0134b73fdb2c091cfb135ad974eb1e07160b0b78bccab42f788  publication.json

attribution-gpu-test-result.json ADDED Viewed

	@@ -0,0 +1,41 @@

+{
+  "adapter_model": "codegeist/codegeist-llm",
+  "adapter_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
+  "adapter_weight_sha256": "4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7",
+  "all_buffers_on_cuda": true,
+  "all_floating_parameters_bfloat16": true,
+  "all_parameters_on_cuda": true,
+  "base_model": "Qwen/Qwen3-1.7B",
+  "base_model_dtype": "bfloat16",
+  "base_revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
+  "device": "cuda",
+  "duration_seconds": 10.726,
+  "expected_response": "Codegeist is a coding agent created by Ren\u00e9 Schmidt.",
+  "hardware": "NVIDIA RTX A2000 12GB",
+  "job": {
+    "accelerator": null,
+    "id": null
+  },
+  "normalization": "strip leading and trailing whitespace",
+  "normalized_match": true,
+  "normalized_response": "Codegeist is a coding agent created by Ren\u00e9 Schmidt.",
+  "peak_cuda_memory_bytes": 3511419904,
+  "prompt": "What is Codegeist?",
+  "raw_response": "Codegeist is a coding agent created by Ren\u00e9 Schmidt.",
+  "runtime": {
+    "packages": {
+      "accelerate": "1.14.0",
+      "huggingface-hub": "1.26.1",
+      "peft": "0.20.0",
+      "safetensors": "0.8.0",
+      "torch": "2.6.0",
+      "transformers": "5.5.0"
+    },
+    "python": "3.12.12"
+  },
+  "source_sha256": {
+    "infer.py": "4b448ee14114b856e55a4639fad0f73110740039c4c334d628a8b44cd06c72c6",
+    "inference/pyproject.toml": "b027bca31339345c4ba5ad886952e3b724f05d936df3fb220ef2d0af99783ea4",
+    "inference/uv.lock": "ebeda66f1193fbdddd4a06c7e3ac3c7789d78c84c224259246e43214b7031bfa"
+  }
+}

evidence.json CHANGED Viewed

@@ -84,7 +84,28 @@
     "repository": "codegeist/codegeist-llm",
     "target_release": "v0.2.0",
     "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
-    "anonymous_gpu_reload_passed": false,
     "historical_v0_1_tags_preserved": true
   },
   "cost_estimate": {
@@ -97,7 +118,7 @@
   "known_gaps": [
     "The training source was not committed at launch; exact source bytes are anchored by SHA-256.",
     "Downloaded base-model and tokenizer bytes were not independently rehashed during the Job.",
-    "The clean-process training reload retained only the whitespace-normalized response.",
     "Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not tested."
   ]
 }

     "repository": "codegeist/codegeist-llm",
     "target_release": "v0.2.0",
     "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
+    "anonymous_gpu_reload_passed": true,
+    "anonymous_gpu_reload": {
+      "hardware": "NVIDIA RTX A2000 12GB",
+      "device": "cuda",
+      "base_model_dtype": "bfloat16",
+      "all_floating_parameters_bfloat16": true,
+      "all_parameters_on_cuda": true,
+      "all_buffers_on_cuda": true,
+      "peak_cuda_memory_bytes": 3511419904,
+      "duration_seconds": 10.726,
+      "raw_response": "Codegeist is a coding agent created by René Schmidt.",
+      "normalized_response": "Codegeist is a coding agent created by René Schmidt.",
+      "normalized_match": true,
+      "token_used": false,
+      "result_sha256": "af0092e72bd347d5a4dd4bfbb579bae0402c51ead31959d33dd5647d4e34a430",
+      "image_id": "sha256:a0f210aed561ed15cb4e44fb7eccde98bc44d354d484005b5f921d85de818f5b",
+      "source_sha256": {
+        "infer.py": "4b448ee14114b856e55a4639fad0f73110740039c4c334d628a8b44cd06c72c6",
+        "inference/pyproject.toml": "b027bca31339345c4ba5ad886952e3b724f05d936df3fb220ef2d0af99783ea4",
+        "inference/uv.lock": "ebeda66f1193fbdddd4a06c7e3ac3c7789d78c84c224259246e43214b7031bfa"
+      }
+    },
     "historical_v0_1_tags_preserved": true
   },
   "cost_estimate": {
   "known_gaps": [
     "The training source was not committed at launch; exact source bytes are anchored by SHA-256.",
     "Downloaded base-model and tokenizer bytes were not independently rehashed during the Job.",
+    "The clean-process training reload retained only the whitespace-normalized response; the later anonymous public reload retained an exact raw response.",
     "Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not tested."
   ]
 }

publication.json CHANGED Viewed

@@ -23,7 +23,23 @@
   "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
   "adapter_weights_changed_from_v0_1": true,
   "anonymous_gpu_reload": {
-    "status": "pending"
   },
   "private_logs_included": false,
   "credentials_included": false

   "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32",
   "adapter_weights_changed_from_v0_1": true,
   "anonymous_gpu_reload": {
+    "status": "passed",
+    "hardware": "NVIDIA RTX A2000 12GB",
+    "all_floating_parameters_bfloat16": true,
+    "all_parameters_on_cuda": true,
+    "all_buffers_on_cuda": true,
+    "peak_cuda_memory_bytes": 3511419904,
+    "duration_seconds": 10.726,
+    "raw_response": "Codegeist is a coding agent created by René Schmidt.",
+    "normalized_match": true,
+    "token_used": false,
+    "result_sha256": "af0092e72bd347d5a4dd4bfbb579bae0402c51ead31959d33dd5647d4e34a430",
+    "image_id": "sha256:a0f210aed561ed15cb4e44fb7eccde98bc44d354d484005b5f921d85de818f5b",
+    "inference_source_sha256": {
+      "infer.py": "4b448ee14114b856e55a4639fad0f73110740039c4c334d628a8b44cd06c72c6",
+      "inference/pyproject.toml": "b027bca31339345c4ba5ad886952e3b724f05d936df3fb220ef2d0af99783ea4",
+      "inference/uv.lock": "ebeda66f1193fbdddd4a06c7e3ac3c7789d78c84c224259246e43214b7031bfa"
+    }
   },
   "private_logs_included": false,
   "credentials_included": false