---

license: mit

pipeline_tag: text-generation

language:

• en

tags:

• 0.4b
• causal-lm
• edge
• gguf
• north-ml
• small-language-model
• wind
• wind-edge

thumbnail: banner.png

---

!Wind Edge 1.6 Gguf

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

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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.

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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)

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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.

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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|

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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>

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Model Details

| Property | Value |

|----------------|-------|

| Parameters | ~0.4B |

| Architecture | Causal LM (decoder-only) |

| Context Length | 8192 tokens |

| Quantization | 1-16bit (GGUF) |

| Org | north-ml1 |

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License

MIT

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Part of North ML - open weights, honest numbers.