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

Libraries

How to use North-ML1/Wind-Edge-1.6-Instruct 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-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("North-ML1/Wind-Edge-1.6-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use North-ML1/Wind-Edge-1.6-Instruct 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-Instruct"
# 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-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/North-ML1/Wind-Edge-1.6-Instruct

SGLang

How to use North-ML1/Wind-Edge-1.6-Instruct 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-Instruct" \
    --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-Instruct",
		"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-Instruct" \
        --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-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use North-ML1/Wind-Edge-1.6-Instruct with Docker Model Runner:
```
docker model run hf.co/North-ML1/Wind-Edge-1.6-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wind-Edge-1.6-Instruct

Wind-Edge-1.6-Instruct is a compact custom Qwen3-compatible assistant model for local and edge inference. It was built from a depth-pruned Wind-Edge base and tuned with a Claude-heavy public distillation SFT mix, code/math instruction data, and a final behavior polish pass.

This is a small model. It is intended for short answers, simple coding help, summaries, and lightweight local assistant use. It is not a replacement for large reasoning models.

Recommended Usage

Use trust_remote_code=True; the custom loader re-applies tied weights from model.safetensors.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "arthu1/Wind-Edge-1.6-Instruct"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you?"}]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

out = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty=1.06,
    eos_token_id=[
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<|im_end|>"),
    ],
)
print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Suggested Settings

For chat:

enable_thinking=False
temperature=0.55-0.7
top_p=0.85-0.92
repetition_penalty=1.05-1.08
max_new_tokens=128-512

For deterministic tests:

do_sample=False
repetition_penalty=1.06
Keep prompts short and direct.

The bundled chat template injects a minimal default identity system message if no system message is supplied:

You are Wind-Edge-1.6, a compact AI assistant model. You are not a human.

Training Summary

Source family: Qwen3-compatible Wind-Edge architecture
Base: depth-pruned and healed Wind-Edge base from Qwen3-0.6B-compatible weights
Final SFT:
- 12M tokens of no-thinking distillation SFT
- Claude-style public distillation data plus OpenOrca, OpenHermes, Open-Platypus, OpenCoder, and OpenMathInstruct
- Bad self-identity teacher rows filtered
- 6M-token system-template adaptation pass
- 2M-token local quality polish for identity, simple arithmetic, list sorting, and concise coding behavior

Quick Sanity Outputs

Expected behavior after the final polish:

hi -> short greeting as Wind-Edge-1.6
Who are you? -> identifies as Wind-Edge-1.6, not human
sort this list: [3, 1, 2] -> [1, 2, 3]
60 miles in 1.5 hours -> 40 mph

Limitations

Wind-Edge-1.6-Instruct is small and can still make arithmetic, factual, and reasoning mistakes. It may overgeneralize from prompts, and it is best used with concise instructions and verification for important work.