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

Libraries

How to use blackdeep/knaif with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="blackdeep/knaif",
	filename="knaif-qwen3-1.7b-v1-q6_k.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 blackdeep/knaif 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 blackdeep/knaif:Q6_K
# Run inference directly in the terminal:
llama cli -hf blackdeep/knaif:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf blackdeep/knaif:Q6_K
# Run inference directly in the terminal:
llama cli -hf blackdeep/knaif:Q6_K

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 blackdeep/knaif:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf blackdeep/knaif:Q6_K

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 blackdeep/knaif:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf blackdeep/knaif:Q6_K

Use Docker

docker model run hf.co/blackdeep/knaif:Q6_K

LM Studio
Jan

vLLM

How to use blackdeep/knaif with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/blackdeep/knaif:Q6_K

Ollama
How to use blackdeep/knaif with Ollama:
```
ollama run hf.co/blackdeep/knaif:Q6_K
```

Unsloth Studio

How to use blackdeep/knaif 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 blackdeep/knaif 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 blackdeep/knaif to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for blackdeep/knaif to start chatting

How to use blackdeep/knaif with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf blackdeep/knaif:Q6_K

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": "blackdeep/knaif:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use blackdeep/knaif with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf blackdeep/knaif:Q6_K

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 blackdeep/knaif:Q6_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use blackdeep/knaif with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf blackdeep/knaif:Q6_K

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 "blackdeep/knaif:Q6_K" \
  --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 blackdeep/knaif with Docker Model Runner:
```
docker model run hf.co/blackdeep/knaif:Q6_K
```

Lemonade

How to use blackdeep/knaif with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull blackdeep/knaif:Q6_K

Run and chat with the model

lemonade run user.knaif-Q6_K

List all available models

lemonade list

knaif / README.md

galind

Update README.md

7c4eb55 verified 6 days ago

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3-4B
  - Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: gguf
tags:
  - knaif
  - planning
  - function-calling
  - gguf

knaif — natural language → validated action plans

Fine-tuned Qwen3 models for knaif: they turn a natural-language request into a strict JSON action plan ({"plan": [...]}) that deterministic code then validates, expands, confirms, and executes through skill packages. The model only proposes the plan — it never runs anything itself.

These are SFT fine-tunes trained on the ffmpeg and documents skills.

Models in this repo

File	Base	Params	Quant	Size	Surface
`knaif-qwen3-4b-v1-q4_k_m.gguf`	Qwen3-4B	4B	Q4_K_M	2.5 GB	desktop / CLI (default)
`knaif-qwen3-1.7b-v1-q6_k.gguf`	Qwen3-1.7B	1.7B	Q6_K	1.4 GB	mobile / low-footprint

Public release names are v1; the underlying fine-tune was training cycle sft-v3-flat.

Intended use

Structured plan generation for knaif skills, not open-ended chat. Given the knaif prompt (available tools + the user request), the model emits only a {"plan": [...]} envelope naming declared tools and arguments. Unknown tools and unsupported arguments are rejected downstream, so the model's job is intent → validated plan, not command execution.

Usage

Via the knaif runtime (recommended — it supplies the prompt and validates/executes the plan):

knaif models pull qwen3-4b-v1        # or qwen3-1.7b-v1
knaif run ffmpeg "compress holiday.mp4 for whatsapp"