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regolo/brick-complexity-2-max-Q8_0-GGUF

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face
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Brick Complexity Classifier v2: max (Q8_0 GGUF)

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What is this?

Q8_0 quantized GGUF of regolo/brick-complexity-2-max. A small classifier that scores each prompt as `easy` / `medium` / `hard` so a router can dispatch it to the right tier of a model pool.

The `max` variant is optimized for routing accuracy: it gives the sharpest easy/medium/hard split so hard queries reliably reach the strongest tier.

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[Regolo.ai](https://regolo.ai) | [Original Model](https://huggingface.co/regolo/brick-complexity-2-max) | [Brick SR1 on GitHub](https://github.com/regolo-ai/brick-SR1)

![License: CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) ![Base Model](https://huggingface.co/Qwen/Qwen3.5-0.8B)

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Model Details

PropertyValue
QuantizationQ8_0
Filebrick-complexity-2-max-Q8_0.gguf
Size775 MB
Bits per weight8.0
Original modelregolo/brick-complexity-2-max
Base modelQwen/Qwen3.5-0.8B
Output classes3 (easy, medium, hard)
LicenseCC BY-NC 4.0

This is a full merged model (base Qwen3.5-0.8B + LoRA adapter merged and quantized), no separate adapter loading needed.

All Available Quantizations

ModelQuantSizeBPW
BF16-GGUFBF161.5 GB16.0
Q8_0-GGUFQ8_0775 MB8.0
Q4_K_M-GGUFQ4KM494 MB5.5

Usage with llama.cpp

bash
huggingface-cli download regolo/brick-complexity-2-max-Q8_0-GGUF brick-complexity-2-max-Q8_0.gguf --local-dir ./models

./llama-cli -m ./models/brick-complexity-2-max-Q8_0.gguf \
    -p "<|im_start|>system
You are a query difficulty classifier for an LLM routing system.
Classify each query as easy, medium, or hard based on the cognitive depth and domain expertise required to answer correctly.
Respond with ONLY one word: easy, medium, or hard.<|im_end|>
<|im_start|>user
Classify: What is the capital of France?<|im_end|>
<|im_start|>assistant
" \
    -n 5 --temp 0

Usage with Ollama

bash
cat > Modelfile <<EOF
FROM ./brick-complexity-2-max-Q8_0.gguf

SYSTEM """You are a query difficulty classifier for an LLM routing system.
Classify each query as easy, medium, or hard based on the cognitive depth and domain expertise required to answer correctly.
Respond with ONLY one word: easy, medium, or hard."""

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
Classify: {{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER temperature 0
PARAMETER num_predict 5
EOF

ollama create brick-complexity-2-max -f Modelfile
ollama run brick-complexity-2-max "Design a distributed consensus algorithm"
# Output: hard

Usage with vLLM

python
from vllm import LLM, SamplingParams

llm = LLM(model="regolo/brick-complexity-2-max-Q8_0-GGUF")
sp = SamplingParams(temperature=0, max_tokens=5)

prompt = """<|im_start|>system
You are a query difficulty classifier for an LLM routing system.
Classify each query as easy, medium, or hard based on the cognitive depth and domain expertise required to answer correctly.
Respond with ONLY one word: easy, medium, or hard.<|im_end|>
<|im_start|>user
Classify: Explain the rendering equation from radiometric first principles<|im_end|>
<|im_start|>assistant
"""

out = llm.generate([prompt], sp)
print(out[0].outputs[0].text.strip())
# Output: hard

Note on GGUF Inference

The GGUF model uses generative text output ("easy"/"medium"/"hard") rather than logit-based classification used by the original LoRA adapter. For maximum accuracy, use the original LoRA adapter with PEFT.

About Brick

Regolo.ai is the EU-sovereign LLM inference platform built on Seeweb infrastructure. Brick is our open-source semantic routing system that intelligently distributes queries across model pools, optimizing for cost, latency, and quality.

[Website](https://regolo.ai) | [Docs](https://docs.regolo.ai) | [GitHub](https://github.com/regolo-ai) | [Discord](https://discord.gg/myuuVFcfJw)