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Model: North-ML1/Wind-Edge-1.6-GGUF
Source: Original Platform

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README.md

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+---
+model-index:
+- name: wind-edge-1.6@f16
+  results:
+  - task:
+      type: text-generation
+      name: Code Generation
+    dataset:
+      name: CodeBench-30
+      type: North-ML1/CodeBench-30
+      split: train
+    metrics:
+    - name: Overall Accuracy
+      type: accuracy
+      value: 6.25
+      verified: false
+    - name: Easy Tier Accuracy
+      type: accuracy
+      value: 17.14
+      verified: false
+    - name: Medium Tier Accuracy
+      type: accuracy
+      value: 0.00
+      verified: false
+    - name: Hard Tier Accuracy
+      type: accuracy
+      value: 0.00
+      verified: false
+
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- wind-edge
+- causal-lm
+- edge
+- small-language-model
+- 0.4b
+license: mit
+datasets:
+- Jackrong/GLM-5.1-Reasoning-1M-Cleaned
+language:
+- en
+base_model:
+- North-ML1/Wind-Edge-1.6-Instruct
+---
+
+# 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](https://huggingface.co/north-ml1)** · [Wind Arc 1.5 Preview](https://huggingface.co/arthu1/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
+
+```python
+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](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.
+
+### 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
+
+```bash
+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](https://huggingface.co/north-ml1) |
+
+---
+
+## License
+
+MIT

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