Instructions to use OmenWorks/bend-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OmenWorks/bend-4b 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 OmenWorks/bend-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OmenWorks/bend-4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OmenWorks/bend-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OmenWorks/bend-4b: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 OmenWorks/bend-4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OmenWorks/bend-4b: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 OmenWorks/bend-4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OmenWorks/bend-4b:Q4_K_M

Use Docker

docker model run hf.co/OmenWorks/bend-4b:Q4_K_M

LM Studio
Jan
Ollama
How to use OmenWorks/bend-4b with Ollama:
```
ollama run hf.co/OmenWorks/bend-4b:Q4_K_M
```
Unsloth Desktop

How to use OmenWorks/bend-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OmenWorks/bend-4b:Q4_K_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": "OmenWorks/bend-4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OmenWorks/bend-4b with Docker Model Runner:
```
docker model run hf.co/OmenWorks/bend-4b:Q4_K_M
```

Lemonade

How to use OmenWorks/bend-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OmenWorks/bend-4b:Q4_K_M

Run and chat with the model

lemonade run user.bend-4b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use OmenWorks/bend-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OmenWorks/bend-4b: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 OmenWorks/bend-4b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OmenWorks/bend-4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OmenWorks/bend-4b: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 "OmenWorks/bend-4b: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"

bend-4b / README.md

jethrovic

Kev-4B-Q4_K_M.gguf: replace our requant (Q4_K cls.output, 93c66545...) with upstream's Q4_K_M (Q8_0 cls.output, 33ae6b18...)

99dba56 verified 6 days ago

preview code

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1.54 kB

metadata

license: apache-2.0
base_model: ggml-org/Kev-4B
tags:
  - gguf
  - decision-model
  - chat
  - llama.cpp

bend-4b — one model, two modes

GGUF quants of Kev-4B (Apache-2.0, upstream: ggml-org/Kev-4B-GGUF) — the 4B model of the Bend shelf.

One model, two modes: a single loaded copy serves chat and structured decisions; switching modes swaps heads on the same model — never a second model, never a reload. See the head shelf: bend-heads.

Files & provenance (stated changes)

Kev-4B-Q8_0.gguf — Q8_0, 4.48 GB — byte-identical mirror of the upstream file (sha256 7c2ebed90560522c…, matches ggml-org/Kev-4B-GGUF). No changes.
Kev-4B-Q4_K_M.gguf — Q4_K_M, 3.03 GB — byte-identical mirror of upstream's own Q4_K_M (sha256 33ae6b18926502b2…, matches ggml-org/Kev-4B-GGUF). No changes. Its decision head cls.output is Q8_0 and its embedding Q4_K: the shape the in-browser (WebGPU) decider reads.
- Replaced 2026-10-17: this path previously held our own 2.71 GB requant (sha256 93c66545eff17854…), whose cls.output had been requantized to Q4_K; the in-browser decider refuses that head, so every decision failed. That file remains in this repo's git history.

License

Apache-2.0 — full text in LICENSE (this repo). Upstream copyright/license notice retained; upstream name credited above and in the filenames. No endorsement by the upstream authors is implied.