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inferencerlabs/
Macaron-V1-Coding-Venti-MLX-Q4.8-INF

Name: Macaron-V1-Coding-Venti-MLX-Q4.8-INF
Availability: InStock

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Macaron-V1-Coding-Venti-Q4.8-INF

See Macaron-V1-Coding-Venti in action: demonstration videos

Tested with an M3 Ultra 512 GiB using Inferencer app

Text Inference: ~14.7 tokens/s @ 1000 tokens ~414.8 GiB

Q4.8-INF uses the data-agnostic INF method tuned to yield maximum general accuracy within a 512 GiB memory budget.

This build uses the fused base model GLM 5.2 with the L2 Coding LoRA from Macaron-V1-Venti.

Quantization (bpw)	Perplexity	Token Accuracy	Missed Divergence
Q4.5	1.35937	89.75%	28.98%
Q4.8	1.26562	93.50%	19.57%
Q4.8-INF	1.21093	97.70%	10.65%
Q5.5	1.24218	94.60%	17.55%
Q6.5	1.21875	96.85%	16.03%
Q8.5	1.21875	97.65%	9.92%
Base	1.20312	100.0%	0.000%

Perplexity: Measures the confidence for predicting base tokens (lower is better).
Token Accuracy: The percentage of correctly generated base tokens.
Missed Divergence: Measures severity of misses; how much the token was missed by.

Quantized with a modified version of MLX.

For more details see our demonstration videos or visit zai-org/GLM-5.2 and mindlab-research/Macaron-V1-Coding-Venti.

Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.

inferencerlabs/Macaron-V1-Coding-Venti-MLX-Q4.8-INF