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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jethrovic/bend-models 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 jethrovic/bend-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jethrovic/bend-models:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/jethrovic/bend-models:Q4_K_M

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

How to use jethrovic/bend-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jethrovic/bend-models: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": "jethrovic/bend-models:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jethrovic/bend-models with Docker Model Runner:
```
docker model run hf.co/jethrovic/bend-models:Q4_K_M
```

Lemonade

How to use jethrovic/bend-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jethrovic/bend-models:Q4_K_M

Run and chat with the model

lemonade run user.bend-models-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jethrovic/bend-models with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jethrovic/bend-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jethrovic/bend-models: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 "jethrovic/bend-models: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-models / models /README.md

jethrovic

G30: models/ MANIFEST + SHA256SUMS + README (imatrix set)

b7c9d17 verified 11 days ago

preview code

Raw History Blame Contribute Delete

2.11 kB

models/: imatrix-calibrated weights (G30, 2026-10-14)

Same quant types and byte budget as the shipped files, re-quantized with an importance matrix. Additive only: the phone app still fetches kev-4b-q4km/ at the repo root, and that dir is unchanged.

file	bytes	sha256	status
`kev-4b-q4km-imat/Kev-4B-Q4_K_M-imat.gguf`	2,709,547,712	`0535181cf3164a7271a7d3d1752a9ceab673c32bb316a081133ec238c186b51c`	ship (llama.cpp path); app NOT repointed
`kev-4b-iq4xs-imat/Kev-4B-IQ4_XS-imat.gguf`	2,514,987,712	`8a45e54a03a689368413de3bb6d33c1129ea416dfa7a8aade64c2f8c2c3b47dc`	published for llama.cpp; native path BLOCKED until gate receipt
`qwen38-27b-q4ks-imat-openjev-wide/Qwen3.8-27B-Q4_K_S-imat-openjev-wide.gguf`	15,586,316,480	`c1e505b2e4266d964cc75e8a801e1c54dfec1bd4205c6ea7072d15c9b907fc0f`	ship (board, llama-server); promotion into the board store is an owner action
`imatrix/kev4b-calib.imatrix.gguf`	3,626,496	`fe439522f2acc99fd040960f045f9582ccb89fe055d570ca883c61600a2f4737`	provenance
`imatrix/qwen38-27b-calib.imatrix.gguf`	13,642,656	`bd707218d6275dbcb342b23846776b306e535f431cb76f316349fa451138488c`	provenance

Fidelity (llama.cpp `--kl-divergence`, 40×512 ctx, 10,200 tokens, held-out lab prose)

artifact	ref	mean KLD	top-1 %
Kev-4B Q4_K_M plain (shipped)	Kev BF16	0.04573	88.25
Kev-4B Q4_K_M-imat	Kev BF16	0.02894 (−37%)	89.81
Kev-4B IQ4_XS-imat	Kev BF16	0.03593	89.22
27B Q4_K_S plain (shipped)	27B Q6_K	0.03652	90.84
27B Q4_K_S-imat	27B Q6_K	0.02126 (−42%)	93.30

The calibration data is in-domain (disjoint from the eval set), so expect a smaller gain on generic text. The decision battery (40 scored questions) shows no regression vs shipped for any of these files.

IQ4_XS-imat: the native llb engine gate is still pending. Use it only with llama.cpp until that gate passes.

Verify: sha256sum -c SHA256SUMS inside models/. MANIFEST.json carries per-file evidence and gate status.

models/: imatrix-calibrated weights (G30, 2026-10-14)

Fidelity (llama.cpp --kl-divergence, 40×512 ctx, 10,200 tokens, held-out lab prose)

Fidelity (llama.cpp `--kl-divergence`, 40×512 ctx, 10,200 tokens, held-out lab prose)