Instructions to use NoemaAI-labs/Noema-Overfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NoemaAI-labs/Noema-Overfit 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 NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NoemaAI-labs/Noema-Overfit:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NoemaAI-labs/Noema-Overfit:UD-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 NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NoemaAI-labs/Noema-Overfit:UD-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 NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NoemaAI-labs/Noema-Overfit:UD-Q4_K_M

Use Docker

docker model run hf.co/NoemaAI-labs/Noema-Overfit:UD-Q4_K_M

LM Studio
Jan
Ollama
How to use NoemaAI-labs/Noema-Overfit with Ollama:
```
ollama run hf.co/NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
```

Unsloth Studio

How to use NoemaAI-labs/Noema-Overfit 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 NoemaAI-labs/Noema-Overfit 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 NoemaAI-labs/Noema-Overfit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NoemaAI-labs/Noema-Overfit to start chatting

How to use NoemaAI-labs/Noema-Overfit with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoemaAI-labs/Noema-Overfit:UD-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": "NoemaAI-labs/Noema-Overfit:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NoemaAI-labs/Noema-Overfit with Docker Model Runner:
```
docker model run hf.co/NoemaAI-labs/Noema-Overfit:UD-Q4_K_M
```

Lemonade

How to use NoemaAI-labs/Noema-Overfit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NoemaAI-labs/Noema-Overfit:UD-Q4_K_M

Run and chat with the model

lemonade run user.Noema-Overfit-UD-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NoemaAI-labs/Noema-Overfit with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoemaAI-labs/Noema-Overfit:UD-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 NoemaAI-labs/Noema-Overfit:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NoemaAI-labs/Noema-Overfit with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NoemaAI-labs/Noema-Overfit:UD-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 "NoemaAI-labs/Noema-Overfit:UD-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"

Noema Overfit

Paged MoE model bundles for Noema's Overfit expert-paging runtime. Each subfolder is one .noema-paged package: a resident.gguf (all non-expert weights, always loaded) plus experts-*.bin page files that are streamed and evicted on demand, described by manifest.json.

These are runtime-specific packages, not standalone GGUF files. Each manifest.json declares its required Noema native contract: the Qwen and Gemma packages below require contract v3, while DeepSeek V4 requires contract v4.

Download the complete .noema-paged folder and preserve its layout. In Noema, add the package to Stored, select Automatic under Overfit (Paged Experts), and run the canary test before relying on it. A fast local SSD is strongly recommended. Expert paging increases runnable capacity; it does not turn storage into RAM or guarantee interactive speed.

Models

`DeepSeek-V4-Flash-0731-UD-IQ4_NL-00001-of-00004.noema-paged/`


Base model	`deepseek-ai/DeepSeek-V4-Flash-0731`
Source GGUF	`unsloth/DeepSeek-V4-Flash-0731-GGUF/UD-IQ4_NL` — 4 shards, 136.66 GB
Upstream revisions	Base `7872f01b1d1fe23eabc4c98b48bffcef5a386062` · GGUF `fbbb5b93fb787c21338159b0af3318bb3f4d9768`
Architecture	`deepseek4` (256 routed experts, 6 active, 43 MoE layers)
Quantization	`UD-IQ4_NL`
Resident weights	`resident.gguf` — 7.81 GB
Expert pages	`experts-000.bin` … `experts-007.bin` — 128.85 GB total
Manifest	Format v1 · native contract v4 · 33,024 records · 16,384-byte alignment
Source SHA-256	`e53b8a27242a271af2ebee6171763e07913fb54dbacb3745afa76e0c50c062ff`
Package fingerprint	`dcd6721b6c851be88ab192280dc9defc855e3f7f31e69b8db3bdcd8024727b12`
Manifest SHA-256	`d9a22388dca29a6abb4c296283f60f7a8b94b88b91d38e1107de05df5d46a32c`
License	MIT (upstream model)

`gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.noema-paged/`


Base model	`google/gemma-4-26B-A4B-it-qat-q4_0-unquantized`
Source GGUF	`unsloth/gemma-4-26B-A4B-it-qat-GGUF` — `UD-Q4_K_XL` (14.25 GB)
Architecture	`gemma4` (128 experts, 8 active, 30 MoE layers, fused gate/up)
Resident weights	`resident.gguf` — 1.40 GB
Expert pages	`experts-000.bin` — 12.96 GB
Alignment	16384 bytes

`Qwen3.6-35B-A3B-UD-Q4_K_M.noema-paged/`


Base model	`Qwen/Qwen3.6-35B-A3B`
Architecture	`qwen35moe` (256 experts, 8 active, 40 MoE layers)
Source GGUF	`Qwen3.6-35B-A3B-UD-Q4_K_M.gguf` (22.13 GB)
Resident weights	`resident.gguf` — 2.57 GB
Expert pages	`experts-000.bin`, `experts-001.bin` — 19.57 GB total
Alignment	16384 bytes

`Qwen3.5-122B-A10B-Q4_K_M.noema-paged/`


Architecture	`qwen35moe` (256 experts, 8 active, 48 MoE layers)
Source GGUF	`Qwen3.5-122B-A10B-Q4_K_M` (2 shards, 74.2 GB)
Resident weights	`resident.gguf` — 4.0 GB
Expert pages	`experts-000.bin` … `experts-004.bin` — 65 GB
Alignment	16384 bytes

Packages are generated with Noema's paged-model conversion tooling. File sizes, source fingerprints, and SHA-256 integrity hashes are recorded in each package's manifest.json.

Licenses

Noema's packaging does not replace the upstream model licenses. Use each bundle according to the terms attached to its base model:

DeepSeek V4 Flash 0731: MIT.
Qwen3.5 and Qwen3.6 bundles: Apache-2.0.
Gemma 4 bundle: Apache-2.0.

Downloads last month: 316

GGUF

Model size

7B params

Architecture

deepseek4

Hardware compatibility

4-bit

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