Multimodal Models
Collection
Multimodal models with leading performance.
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17 items
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Updated
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This is the int4 quantized version of MiniCPM-o 2.6.
Running with int4 version would use lower GPU memory (about 9GB).
We are submitting PR to officially support minicpm-o 2.6 inference
git clone https://github.com/OpenBMB/AutoGPTQ.git && cd AutoGPTQ
git checkout minicpmo
# install AutoGPTQ
pip install -vvv --no-build-isolation -e .
Change the model initialization part to AutoGPTQForCausalLM.from_quantized
import torch
from transformers import AutoModel, AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model = AutoGPTQForCausalLM.from_quantized(
'openbmb/MiniCPM-o-2_6-int4',
torch_dtype=torch.bfloat16,
device="cuda:0",
trust_remote_code=True,
disable_exllama=True,
disable_exllamav2=True
)
tokenizer = AutoTokenizer.from_pretrained(
'openbmb/MiniCPM-o-2_6-int4',
trust_remote_code=True
)
model.init_tts()
Usage reference MiniCPM-o-2_6#usage