Instructions to use North-ML1/Wind-Edge-1.6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use North-ML1/Wind-Edge-1.6-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="North-ML1/Wind-Edge-1.6-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("North-ML1/Wind-Edge-1.6-GGUF", dtype="auto")

llama-cpp-python

How to use North-ML1/Wind-Edge-1.6-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="North-ML1/Wind-Edge-1.6-GGUF",
	filename="Wind-Edge-1.6-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use North-ML1/Wind-Edge-1.6-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf North-ML1/Wind-Edge-1.6-GGUF: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 North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf North-ML1/Wind-Edge-1.6-GGUF: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 North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

Use Docker

docker model run hf.co/North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use North-ML1/Wind-Edge-1.6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "North-ML1/Wind-Edge-1.6-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "North-ML1/Wind-Edge-1.6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

SGLang

How to use North-ML1/Wind-Edge-1.6-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "North-ML1/Wind-Edge-1.6-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "North-ML1/Wind-Edge-1.6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "North-ML1/Wind-Edge-1.6-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "North-ML1/Wind-Edge-1.6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use North-ML1/Wind-Edge-1.6-GGUF with Ollama:
```
ollama run hf.co/North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
```

Unsloth Studio new

How to use North-ML1/Wind-Edge-1.6-GGUF 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 North-ML1/Wind-Edge-1.6-GGUF 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 North-ML1/Wind-Edge-1.6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for North-ML1/Wind-Edge-1.6-GGUF to start chatting

Pi new

How to use North-ML1/Wind-Edge-1.6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf North-ML1/Wind-Edge-1.6-GGUF: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": "North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use North-ML1/Wind-Edge-1.6-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf North-ML1/Wind-Edge-1.6-GGUF: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 North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use North-ML1/Wind-Edge-1.6-GGUF with Docker Model Runner:
```
docker model run hf.co/North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M
```

Lemonade

How to use North-ML1/Wind-Edge-1.6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull North-ML1/Wind-Edge-1.6-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Wind-Edge-1.6-GGUF-Q4_K_M

List all available models

lemonade list

Wind Edge 1.6 — Geode (0.4B)

A 0.4B parameter causal language model built for edge deployment. Fast, small, and honest about what it can do.

North ML · Wind Arc 1.5 Preview

Overview

Wind Edge 1.6 (Geode) is a compact LLM trained for real-time, on-device inference. At 0.4B parameters it sits in the ultra-small tier — expect strong common-sense and classification performance, limited hard reasoning.

Best use cases:

Instruction-following dialogue (short to medium turns)
Text classification and sentiment
Light code completion
Summarization of short passages

Not recommended for: multi-step math, complex logical chains, long-context tasks.

Changes vs 1.5

Improved instruction adherence on structured output formats
More stable multi-sentence generation (fewer mid-sequence repetitions)
Reduced hallucination rate on short factual queries (internal held-out eval)

Honest Benchmark Estimates

Realistic ranges for a well-trained 0.4B model — not cherry-picked numbers.

Task	Expected Range	Notes
Common Sense (0-shot)	0.60 – 0.68	Reliable strength
Sentiment Analysis	0.70 – 0.80	Reliable strength
Text Classification	0.68 – 0.78	Reliable strength
Reading Comprehension	0.52 – 0.63	Context-dependent
Summarization	0.58 – 0.68	Short docs only
Code Generation	0.45 – 0.58	Simple tasks only
Math Reasoning	0.15 – 0.28	Known weak point at this scale
Logical Reasoning	0.18 – 0.28	Known weak point at this scale

A 0.4B model cannot compete with 7B+ on reasoning — Geode doesn't pretend to.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("north-ml1/wind-edge-1.6")
tokenizer = AutoTokenizer.from_pretrained("north-ml1/wind-edge-1.6")

inputs = tokenizer("You are Wind Edge, a helpful AI assistant.\nUser: ", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=256, temperature=0.6, top_p=0.9)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Recommended Settings

Parameter	Value
temperature	0.0
top_p	0.95
min_p	0.05
max_new_tokens	256–512
repetition_penalty	1.1
context_limit	1024-4096

GGUF Quantizations

GGUF quants converted from arthu1/Wind-Edge-1.6-Instruct using a Qwen3-compatible tensor layout. The Transformers repo remains canonical — use these for llama.cpp, LM Studio, Ollama-style runtimes, and any other GGUF-compatible inference stack.

Files

File	bpw	Use
Wind-Edge-1.6-TQ1_0.gguf	~1.7 bpw	Experimental 1-bit/ternary. Lowest quality, smallest size.
Wind-Edge-1.6-TQ2_0.gguf	~2.1 bpw	Very small 2-bit/ternary option.
Wind-Edge-1.6-IQ3_M.gguf	~3.7 bpw	Good balance for tiny devices.
Wind-Edge-1.6-Q4_K_M.gguf	~4.6 bpw	Recommended default.
Wind-Edge-1.6-Q6_K.gguf	~6.1 bpw	Higher quality, still compact.
Wind-Edge-1.6-Q8_0.gguf	~8.5 bpw	Near-lossless practical quant.
Wind-Edge-1.6-F16.gguf	16 bpw	Full precision GGUF export.

Q4_K_M, Q6_K, and Q8_0 are the recommended daily drivers. TQ1_0 and TQ2_0 are included for constrained edge hardware but will lose measurable reasoning and factual accuracy.

llama.cpp

llama-cli \
  -m Wind-Edge-1.6-Q4_K_M.gguf \
  -cnv \
  --temp 0.6 \
  --top-p 0.9 \
  --repeat-penalty 1.06 \
  -n 512

For deterministic output, use --temp 0 and keep prompts short.

Chat Template

The GGUF metadata includes the chat template. If your runtime doesn't apply it automatically:

<|im_start|>system
You are Wind-Edge-1.6, a compact AI assistant model. You are not a human.<|im_end|>
<|im_start|>user
Who are you?<|im_end|>
<|im_start|>assistant
<think>
</think>

Model Details

Property	Value
Parameters	~0.4B
Architecture	Causal LM (decoder-only)
Context Length	8192 tokens
Quantization	1-16bit (GGUF)
Org	north-ml1

License

MIT

Downloads last month: 2,704

GGUF

Model size

0.4B params

Architecture

qwen3

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

6-bit

8-bit

16-bit

Model tree for North-ML1/Wind-Edge-1.6-GGUF

Base model

North-ML1/Wind-Edge-1.6-Base

Finetuned

North-ML1/Wind-Edge-1.6-Instruct

Quantized

(1)

this model

Dataset used to train North-ML1/Wind-Edge-1.6-GGUF

Collection including North-ML1/Wind-Edge-1.6-GGUF

North ML Version 1

Collection

3 items • Updated 21 days ago

Evaluation results

Overall Accuracy on CodeBench-30
self-reported

6.250
Easy Tier Accuracy on CodeBench-30
self-reported

17.140
Medium Tier Accuracy on CodeBench-30
self-reported

0.000
Hard Tier Accuracy on CodeBench-30
self-reported

0.000