Wind Edge 1.7
The Final Traditional LLM. Not the Final AI.
Wind Edge 1.7 is a new language model developed by North ML, representing the final planned traditional large language model in our research roadmap.
Wind Edge 1.7 concludes a chapter of North ML's development history, spanning dozens of experimental prototypes, model families, and research initiatives.
However, this does not mean North ML is abandoning language models, generative AI, or text generation.
Instead, we are transitioning toward an entirely new research direction: Veyrian Execution Generators (VEGs).
Our goal is to move beyond the limitations of conventional architectures while continuing to develop AI systems that understand, reason, and generate text.
Model Overview
| Property | Description |
|---|---|
| Model | Wind Edge 1.7 |
| Organization | North ML |
| Family | Wind |
| Model Category | Generative Language Model |
| Generation | 1.7 |
| Research Focus | Efficient language generation |
| Status | New model |
| Future Research | Veyrian Execution Generators |
| Planned Successor | Wind Flame 2.0 |
Technical Specifications
| Property | Value |
|---|---|
| Parameters | ~205M |
| Architecture | Decoder-only Transformer (Qwen3-compatible) |
| Layers | 18 |
| Hidden size | 768 |
| Attention | GQA — 12 query / 4 KV heads |
| Position encoding | RoPE (theta 500,000) |
| Context length | 2,048 tokens |
| Vocabulary | 32,000 (Llama BPE) |
| Chat format | ChatML (`< |
| License | Apache-2.0 |
Available Files
| File | Description |
|---|---|
model.safetensors |
Full weights, BF16, Qwen3-compatible layout |
chat_template.jinja |
ChatML chat template |
tokenizer.json / tokenizer.model |
Llama BPE tokenizer (32k vocab) |
A GGUF version (Q4_K_M / Q8_0 / BF16, incl. the step-500 checkpoint) is available at North-ML1/wind-edge-1.7-gguf, and the pretrained base checkpoint at arthu1/wind-lite-1-6-0326-beta1.
Usage
transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("North-ML1/wind-edge-1.7")
model = AutoModelForCausalLM.from_pretrained("North-ML1/wind-edge-1.7")
messages = [{"role": "user", "content": "What is 197 + 486?"}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=100)
print(tok.decode(out[0]))
Example prompt format
<|im_start|>user
What is 197 + 486?<|im_end|>
<|im_start|>assistant
The End of the Traditional Wind LLM Era
North ML has developed numerous experimental models, architectures, and prototypes throughout its research history.
Our work has explored different approaches to model efficiency, language understanding, reasoning, and accessible AI.
Wind Edge 1.7 represents the final planned traditional LLM in this journey.
It is not the end of the Wind family. It is the beginning of a transition toward something more ambitious.
The next generation of Wind models will still be generative AI systems. They will simply investigate a different architectural foundation.
Introducing Veyrian Execution Generators (VEGs)
A Veyrian Execution Generator, abbreviated VEG, is a proposed new class of generative artificial intelligence models being researched by North ML.
VEGs are designed to understand and generate natural language, perform reasoning, solve problems, and carry out complex intelligent tasks.
Unlike traditional Transformer-based language models, VEGs aim to use novel computational architectures and execution mechanisms to pursue greater intelligence with fewer resources.
Core Research Goals
- Frontier-Level Intelligence — VEGs aim to achieve extremely advanced reasoning, knowledge processing, and problem-solving capabilities, potentially competing with future frontier AI systems.
- Native Text Generation — VEGs remain generative AI models. They are intended to generate text directly, hold conversations, explain concepts, write code, and perform language-based tasks without requiring conversion into a Transformer.
- Improved Parameter Efficiency — Our objective is to achieve greater capabilities using fewer parameters and less computation than comparable conventional architectures.
- Novel Architecture — VEGs will explore computational approaches that differ from standard Transformer-based models. This research may include new processing structures, execution mechanisms, and approaches to representing and generating information.
- Architectural Convertibility — A key research objective is to investigate whether VEG models can be converted into conventional Transformer-based LLMs. Such conversion could enable deployment across existing language model infrastructure while retaining useful capabilities developed in a VEG.
- Advanced Reasoning — VEGs are intended to prioritize reasoning quality, complex problem-solving, and adaptive computation rather than focusing exclusively on parameter count.
These objectives remain experimental and do not represent established performance claims.
Wind Flame 2.0: The VEG Generation
Wind Flame 2.0 is North ML's planned VEG model and a major step in our next research direction.
It is intended to introduce a different approach to generative intelligence while retaining core capabilities associated with modern language models.
Planned Capabilities
| Capability | Wind Flame 2.0 Goal |
|---|---|
| Text generation | Native |
| Natural language understanding | Yes |
| Conversational AI | Yes |
| Code generation | Targeted |
| Advanced reasoning | Primary research goal |
| Novel architecture | VEG |
| Transformer conversion | Planned research capability |
| Parameter efficiency | Primary optimization goal |
| Frontier intelligence | Long-term objective |
VEG-to-Transformer Conversion
One of the most ambitious objectives for Wind Flame 2.0 is the possibility of converting a trained VEG into a conventional Transformer-based LLM.
The proposed process would allow a model to operate in two architectural forms:
- Native VEG: A generative AI model using the Veyrian architecture.
- Converted Transformer: A conventional Transformer-based language model derived from the VEG.
The objective is to preserve as much useful intelligence and functionality as possible across the conversion.
Whether this can be achieved efficiently and without significant capability loss is an open research question.
Wind Edge 1.7 vs. Wind Flame 2.0
| Category | Wind Edge 1.7 | Wind Flame 2.0 |
|---|---|---|
| Generation | Traditional LLM era | VEG era |
| Architecture | Conventional language-model approach | Novel Veyrian architecture |
| Text generation | Yes | Yes |
| Reasoning | Language-model capabilities | Advanced reasoning target |
| Transformer conversion | Not a defining objective | Planned |
| Research direction | Traditional LLM development | Next-generation generative AI |
Wind Flame 2.0 is not intended to eliminate language generation. It is intended to explore how language-generating intelligence can be built differently.
Our Vision
North ML believes that the future of artificial intelligence should not depend exclusively on increasing parameter counts, training budgets, and infrastructure requirements.
Our research aims to investigate whether new architectural designs can provide substantial improvements in intelligence, efficiency, and accessibility.
With Wind Edge 1.7, we conclude our current traditional LLM roadmap. With Veyrian, we intend to explore a new generation of generative AI systems.
Our ambition is not simply to build larger models. It is to build smarter models.
Research and Development Status
Wind Edge 1.7 is part of North ML's ongoing Wind development efforts.
Technical specifications, training methodology, benchmark results, supported configurations, and model availability will be documented as they are confirmed.
Veyrian Execution Generators and Wind Flame 2.0 are planned research initiatives. Their proposed architecture and capabilities have not yet been independently demonstrated.
This documentation distinguishes current model information from future research objectives.
About North ML
North ML is an independent artificial intelligence research project focused on developing efficient, capable, and accessible AI systems.
Our work includes experimental architectures, language models, model optimization, and research into new approaches to generative intelligence.
Explore our models and research: North ML on Hugging Face
A New Era Begins
Wind Edge 1.7 is the last planned traditional LLM from North ML. Wind Flame 2.0 is planned as the beginning of our VEG generation.
We are not leaving generative AI behind. We are exploring what it could become.
North ML — Beyond Conventional Intelligence.
- Downloads last month
- 292