Instructions to use pointbreaklab/knot-delta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pointbreaklab/knot-delta with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pointbreaklab/knot-delta",
	filename="kd-delta-9b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pointbreaklab/knot-delta 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 pointbreaklab/knot-delta:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pointbreaklab/knot-delta:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/pointbreaklab/knot-delta:Q4_K_M

LM Studio
Jan

vLLM

How to use pointbreaklab/knot-delta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pointbreaklab/knot-delta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pointbreaklab/knot-delta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pointbreaklab/knot-delta:Q4_K_M

Ollama
How to use pointbreaklab/knot-delta with Ollama:
```
ollama run hf.co/pointbreaklab/knot-delta:Q4_K_M
```

Unsloth Studio

How to use pointbreaklab/knot-delta 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 pointbreaklab/knot-delta 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 pointbreaklab/knot-delta to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pointbreaklab/knot-delta to start chatting

How to use pointbreaklab/knot-delta with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pointbreaklab/knot-delta: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": "pointbreaklab/knot-delta:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pointbreaklab/knot-delta with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pointbreaklab/knot-delta: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 pointbreaklab/knot-delta:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use pointbreaklab/knot-delta with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pointbreaklab/knot-delta: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 "pointbreaklab/knot-delta: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 pointbreaklab/knot-delta with Docker Model Runner:
```
docker model run hf.co/pointbreaklab/knot-delta:Q4_K_M
```

Lemonade

How to use pointbreaklab/knot-delta with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pointbreaklab/knot-delta:Q4_K_M

Run and chat with the model

lemonade run user.knot-delta-Q4_K_M

List all available models

lemonade list

Knot Delta

On-device model that explains what changed. Give it a commit and Knot Delta writes a grounded report: what moved, why, and the risk. A 9B model, served as a single quantized GGUF that runs on your machine.

Part of the Knot AI family, alongside Knot Scribe (which writes your commit messages). Delta is the change-intelligence layer inside Knot.

A fine-tune of Qwen3.5-9B, merged and exported to a text-only Q4_K_M GGUF (~5.2 GB) for llama.cpp / Ollama.

Capability scorecard (honest)

Every number is measured against objective ground truth (real git history), reported beside the baseline a lazy model would score. No aggregate "intelligence score" is quoted. Frozen held-out set: 325 commits across 9 unseen real repos (vite, vue, svelte, astro, ionic, gumroad, OpenDream, radicle, vllm).

capability	number	baseline	verdict
Change-type (intent read from code), macro-F1	0.409	majority-class 0.032	real — 12.9× base rate (n=235)
Cited files that exist in the diff	100%	—	never hallucinates a file; holds on unseen Go
Reports fully grounded	98.5%	—	325 held-out commits (first-pass 88%)
Exact-line precision	0.64	0.94 lenient (right region)	honest limit: file + region reliable, exact line ~64%

Multi-repo generalization (exact-line precision, 4 unseen repos):

repo	language	verified	line precision
flask	Python	11/12	0.74
gin	Go (unseen)	12/12	0.70
ripgrep	Rust	12/12	0.52
frozen-325	JS/TS +	320/325	0.64

Honest read: Delta never cites a file that isn't in the diff (100% across every repo, including Go, which is absent from its training data), and it always points at the right region. What it does not yet do reliably is pin the exact line — that lands 52–74% of the time, lowest on Rust. It knows which file and which part changed; the precise line is still improving. One capability, measured straight, with the parts that don't yet work said out loud.

Use it

llama.cpp

llama-server --model kd-delta-9b-Q4_K_M.gguf

Ollama

ollama run pointbreaklab/knot-delta

Model

Base: Qwen3.5-9B
Format: text-only Q4_K_M GGUF, ~5.2 GB
Runs: llama.cpp / Ollama, CPU or GPU, fully local
License: Apache-2.0

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for pointbreaklab/knot-delta

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

unsloth/Qwen3.5-9B

Quantized

(21)

this model