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amd/gpt-oss-120b-w-mxfp4-a-fp8
gpt-oss-120b-w-mxfp4-a-fp8 is a machine learning model from amd. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
- Model Architecture: gpt-oss-120b - Input: Text - Output: Text - Supported Hardware Microarchitecture: AMD MI350/MI355 - ROCm: 7.0 - Operating System(s): Linux - Inference Engine: vLLM - Model Optimizer: AMD-Quark
Downloads · 30 days
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From the Hugging Face model README
This model was built with gpt-oss-120b model by applying AMD-Quark for MXFP4 quantization.
The model was quantized from openai/gpt-oss-120b using AMD-Quark. The weights are quantized MXFP4 and activations were quantized to FP8.
Quantization scripts:
cd Quark/examples/torch/language_modeling/llm_ptq/
exclude_layers="*lm_head *self_attn* *router*"
python3 internal_scripts/quantize_quark.py \
--model_dir openai/gpt-oss-120b \
--quant_scheme w_mxfp4_a_fp8 \
--exclude_layers $exclude_layers \
--num_calib_data 512 \
--output_dir amd/gpt-oss120b-w-mxfp4-a-fp8 \
--model_export hf_format \
--multi_gpu
This model can be deployed efficiently using the vLLM backend.
The model was evaluated on AIME25 and GPQA Diamond benchmarks with low reasoning effort.
The results of AIME25 and GPQA Diamond were obtained using gpt_oss.evals with low effort setting, and vLLM docker rocm/vllm-private:rocm7.0_ubuntu_22.04_vllm_0.10.1_instinct_gptoss_wmxfp4_afp8_20251030.
vllm serve amd/gpt-oss120b-w-mxfp4-a-fp8 \
--tensor_parallel_size 2 \
--gpu-memory-utilization 0.90 \
--no-enable-prefix-caching \
--max-num-batched-tokens 1024
python -m gpt_oss.evals --model /shareddata/amd/gpt-oss120b-w-mxfp4-a-fp8 --eval aime25,gpqa --reasoning-effort low --n-threads 128
Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.