gpt-oss-20B-MXFP4-MOE-GGUF – AI Model by FreedomAISVR | AlphaNeural AI

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FreedomAISVR/
gpt-oss-20B-MXFP4-MOE-GGUF

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mixture-of-experts

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openai/gpt-oss-20b

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gpt-oss-20B-MXFP4-MOE-GGUF

MXFP4_MOE GGUF quantization of openai/gpt-oss-20b.

About MXFP4_MOE

MXFP4_MOE is the MoE variant of MXFP4 (OCP Microscaling FP4 E2M1 format). It applies a mixed-precision quantization:

Tensor Group	Quantization	Bits/Param
Expert FFN weights	MXFP4 (block FP4)	~4.25
Attention weights	Q8_0	8
Router/norms/biases	F32	32

Total: ~4.63 bits/param — the file is 11.28 GiB for 20.91B total parameters.

MXFP4 runs on any GPU or CPU (unlike NVFP4 which requires Blackwell).

Files

Filename	Type	Size	Description
`gpt-oss-20B-MXFP4_MOE.gguf`	MXFP4_MOE	11.28 GiB	Main model weights

Usage

llama.cpp CLI

llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF -cnv -p "You are a helpful assistant"

llama-server

llama-server -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF --ctx-size 0 --jinja

llama-cpp-python

python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF",
    filename="gpt-oss-20B-MXFP4_MOE.gguf",
)

Quantization Pipeline

bash

# 1. Convert HF model to intermediate GGUF
python convert_hf_to_gguf.py ./models/gpt-oss-20b/ --outfile gpt-oss-20b-f16.gguf --outtype f16

# 2. Quantize to MXFP4_MOE (requires --allow-requantize due to converter auto-type assignment)
llama-quantize --allow-requantize gpt-oss-20b-f16.gguf gpt-oss-20b-mxfp4_moe.gguf MXFP4_MOE

Hardware

GPU	VRAM	Notes
NVIDIA RTX 5060 Ti	16 GB	Quantization performed on this GPU

License

Apache-2.0 (same as openai/gpt-oss-20b)