Model name
MiniMaxAI/MiniMax-M3
Model variant / size
427B
Model link
https://huggingface.co/MiniMaxAI/MiniMax-M3
What do you want AutoRound to support?
Quantization / rounding workflow support for this model
Motivation & use case
quantize model with non model-free method
The config.json generated by the quantized model appears to be incorrect. Could you explain why the following configuration is present?
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"minimax_m3_sparse",
"minimax_m3_sparse",
"minimax_m3_sparse",
"minimax_m3_sparse"
]
This causes VLLM to fail when loading the model. However, if I manually remove the layer_types field (or the related entries), the model can be loaded successfully, but the GSM8K accuracy drops to 0.
Is this a known issue in the quantization/export process, or is there a problem with how config.json is generated?
Environment (optional but helpful)
No response
Current blockers / errors (if any)
No response
Definition of Done (acceptance criteria)
No response
Additional context
No response
Model name
MiniMaxAI/MiniMax-M3
Model variant / size
427B
Model link
https://huggingface.co/MiniMaxAI/MiniMax-M3
What do you want AutoRound to support?
Quantization / rounding workflow support for this model
Motivation & use case
quantize model with non model-free method
The
config.jsongenerated by the quantized model appears to be incorrect. Could you explain why the following configuration is present?This causes VLLM to fail when loading the model. However, if I manually remove the
layer_typesfield (or the related entries), the model can be loaded successfully, but the GSM8K accuracy drops to 0.Is this a known issue in the quantization/export process, or is there a problem with how
config.jsonis generated?Environment (optional but helpful)
No response
Current blockers / errors (if any)
No response
Definition of Done (acceptance criteria)
No response
Additional context
No response