Instructions to use mkleinegger/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mkleinegger/tmp 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 mkleinegger/tmp:IQ1_M
# Run inference directly in the terminal:
llama cli -hf mkleinegger/tmp:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mkleinegger/tmp:IQ1_M
# Run inference directly in the terminal:
llama cli -hf mkleinegger/tmp:IQ1_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 mkleinegger/tmp:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf mkleinegger/tmp:IQ1_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 mkleinegger/tmp:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mkleinegger/tmp:IQ1_M

Use Docker

docker model run hf.co/mkleinegger/tmp:IQ1_M

LM Studio
Jan
Ollama
How to use mkleinegger/tmp with Ollama:
```
ollama run hf.co/mkleinegger/tmp:IQ1_M
```
Unsloth Desktop

How to use mkleinegger/tmp with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkleinegger/tmp:IQ1_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mkleinegger/tmp:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mkleinegger/tmp with Docker Model Runner:
```
docker model run hf.co/mkleinegger/tmp:IQ1_M
```

Lemonade

How to use mkleinegger/tmp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mkleinegger/tmp:IQ1_M

Run and chat with the model

lemonade run user.tmp-IQ1_M

List all available models

lemonade list

Hermes Agent

How to use mkleinegger/tmp with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkleinegger/tmp:IQ1_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 mkleinegger/tmp:IQ1_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mkleinegger/tmp with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mkleinegger/tmp:IQ1_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 "mkleinegger/tmp:IQ1_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"

tmp / README.md

mkleinegger

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Qwen3.8-27B GSQ + RCO — data artifact

Use this folder with qwen3.8-27b-gsq-rco-code.zip. Extract that ZIP next to this folder, or set GSQ_RCO_DATA_DIR to this folder's absolute path.

Contents:

models/Qwen3.8-27B/: complete original checkpoint, tokenizer, model configuration, and model license; 18 safetensors shards, pinned to revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.
qwen3.8-27b-gguf-uniform/: all 12 uniform GGUF model files.
calibration/fineweb_edu.pt: 1,024 token sequences of length 2,048, with sampling metadata in fineweb_edu.json.
calibration/wikitext2/: saved train, validation, and test splits for offline evaluation.
model-metadata/: Qwen3.8-27B SSM permutations and linear-module shapes.
reference-assignments/: saved RCO assignments and the GSQ type mapping.
model-manifest.json and input-manifest.json: checkpoint revision, available checkpoint hashes, and GGUF file-size records.
data-copy-manifest.json: all 63 copied input files, sizes, and copy checks (full SHA256 for files up to 8 MiB; matching first/last 1 MiB for larger files).

Weights and GGUFs total about 170.4 GB, plus a small amount of dataset and metadata space. These are independent copies, not links into the original bundle. The derived candidate store is not included; build it with the code artifact's build_store.sh on a disk with about 591 GB additional capacity, plus room for later search caches and GSQ outputs.

The folder contains no Python environment or source repository. The original combined bundle is preserved.

Existing staged models in /home/mhelcig/data/Qwen3.8-27B-GSQ-RCO are excluded. In particular, the staged files named GSQ-RCO-IQ2_S and GSQ-RCO-IQ2_XS were checked and contain multiple quantization types across their linear layers: their filenames do not mean they are uniform models. Mixed-bit, block/code-pruned variants, and attic/ contents are not included.