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Model: North-ML1/Wind-Edge-1.6-GGUF Source: Original Platform
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---
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model-index:
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- name: wind-edge-1.6@f16
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results:
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- task:
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type: text-generation
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name: Code Generation
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dataset:
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name: CodeBench-30
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type: North-ML1/CodeBench-30
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split: train
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metrics:
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- name: Overall Accuracy
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type: accuracy
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value: 6.25
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verified: false
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- name: Easy Tier Accuracy
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type: accuracy
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value: 17.14
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verified: false
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- name: Medium Tier Accuracy
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type: accuracy
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value: 0.00
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verified: false
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- name: Hard Tier Accuracy
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type: accuracy
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value: 0.00
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verified: false
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- wind-edge
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- causal-lm
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- edge
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- small-language-model
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- 0.4b
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license: mit
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datasets:
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- Jackrong/GLM-5.1-Reasoning-1M-Cleaned
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language:
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- en
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base_model:
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- North-ML1/Wind-Edge-1.6-Instruct
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---
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# Wind Edge 1.6 — Geode (0.4B)
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A 0.4B parameter causal language model built for edge deployment. Fast, small, and honest about what it can do.
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**[North ML](https://huggingface.co/north-ml1)** · [Wind Arc 1.5 Preview](https://huggingface.co/arthu1/wind-arc-1-5-preview)
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---
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## Overview
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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.
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**Best use cases:**
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- Instruction-following dialogue (short to medium turns)
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- Text classification and sentiment
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- Light code completion
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- Summarization of short passages
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**Not recommended for:** multi-step math, complex logical chains, long-context tasks.
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---
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## Changes vs 1.5
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- Improved instruction adherence on structured output formats
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- More stable multi-sentence generation (fewer mid-sequence repetitions)
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- Reduced hallucination rate on short factual queries (internal held-out eval)
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---
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## Honest Benchmark Estimates
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Realistic ranges for a well-trained 0.4B model — not cherry-picked numbers.
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| Task | Expected Range | Notes |
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|-----------------------|----------------|-------|
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| Common Sense (0-shot) | 0.60 – 0.68 | Reliable strength |
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| Sentiment Analysis | 0.70 – 0.80 | Reliable strength |
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| Text Classification | 0.68 – 0.78 | Reliable strength |
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| Reading Comprehension | 0.52 – 0.63 | Context-dependent |
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| Summarization | 0.58 – 0.68 | Short docs only |
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| Code Generation | 0.45 – 0.58 | Simple tasks only |
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| Math Reasoning | 0.15 – 0.28 | Known weak point at this scale |
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| Logical Reasoning | 0.18 – 0.28 | Known weak point at this scale |
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A 0.4B model cannot compete with 7B+ on reasoning — Geode doesn't pretend to.
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("north-ml1/wind-edge-1.6")
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tokenizer = AutoTokenizer.from_pretrained("north-ml1/wind-edge-1.6")
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inputs = tokenizer("You are Wind Edge, a helpful AI assistant.\nUser: ", return_tensors="pt")
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output = model.generate(**inputs, max_new_tokens=256, temperature=0.6, top_p=0.9)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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### Recommended Settings
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| Parameter | Value |
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|--------------------|----------|
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| temperature | 0.0 |
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| top_p | 0.95 |
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| min_p | 0.05 |
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| max_new_tokens | 256–512 |
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| repetition_penalty | 1.1 |
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| context_limit | 1024-4096|
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---
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## GGUF Quantizations
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GGUF quants converted from [arthu1/Wind-Edge-1.6-Instruct](https://huggingface.co/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.
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### Files
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| File | bpw | Use |
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|------|-----|-----|
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| Wind-Edge-1.6-TQ1_0.gguf | ~1.7 bpw | Experimental 1-bit/ternary. Lowest quality, smallest size. |
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| Wind-Edge-1.6-TQ2_0.gguf | ~2.1 bpw | Very small 2-bit/ternary option. |
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| Wind-Edge-1.6-IQ3_M.gguf | ~3.7 bpw | Good balance for tiny devices. |
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| Wind-Edge-1.6-Q4_K_M.gguf | ~4.6 bpw | **Recommended default.** |
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| Wind-Edge-1.6-Q6_K.gguf | ~6.1 bpw | Higher quality, still compact. |
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| Wind-Edge-1.6-Q8_0.gguf | ~8.5 bpw | Near-lossless practical quant. |
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| Wind-Edge-1.6-F16.gguf | 16 bpw | Full precision GGUF export. |
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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.
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### llama.cpp
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```bash
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llama-cli \
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-m Wind-Edge-1.6-Q4_K_M.gguf \
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-cnv \
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--temp 0.6 \
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--top-p 0.9 \
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--repeat-penalty 1.06 \
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-n 512
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```
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For deterministic output, use `--temp 0` and keep prompts short.
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### Chat Template
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The GGUF metadata includes the chat template. If your runtime doesn't apply it automatically:
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```
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<|im_start|>system
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You are Wind-Edge-1.6, a compact AI assistant model. You are not a human.<|im_end|>
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<|im_start|>user
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Who are you?<|im_end|>
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<|im_start|>assistant
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<think>
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</think>
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```
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---
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## Model Details
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| Property | Value |
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|----------------|-------|
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| Parameters | ~0.4B |
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| Architecture | Causal LM (decoder-only) |
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| Context Length | 8192 tokens |
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| Quantization | 1-16bit (GGUF) |
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| Org | [north-ml1](https://huggingface.co/north-ml1) |
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---
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## License
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MIT
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Wind-Edge-1.6-F16.gguf
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