regolo/brick-complexity-2-max-Q8_0-GGUF
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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)
 
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Model Details
This is a full merged model (base Qwen3.5-0.8B + LoRA adapter merged and quantized), no separate adapter loading needed.
All Available Quantizations
Usage with llama.cpp
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 0Usage with Ollama
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: hardUsage with vLLM
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: hardNote 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)
