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SmolVLM-256M-Instruct-GPTQ-INT4-v2 – AI Model by Azaz666 | AlphaNeural AI

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Azaz666/
SmolVLM-256M-Instruct-GPTQ-INT4-v2

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vision-language-model

HuggingFaceTB/SmolVLM-256M-Instruct

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HuggingFaceTBSmolVLM-256M-Instructgptq_int4_merged

This is a GPTQ (4-bit) quantized version of HuggingFaceTB/SmolVLM-256M-Instruct.

Quantization Details

Method: GPTQ
Bits: 4
Base model: HuggingFaceTB/SmolVLM-256M-Instruct
Group size: 128
Quantized modules: text decoder + lm_head
FP16 modules: vision encoder, connector
Approach: Extract inner Llama -> GPTQ -> re-attach to full Idefics3

Usage

python

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model = AutoModelForImageTextToText.from_pretrained(
    "{REPO_ID}",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained("{REPO_ID}", trust_remote_code=True)

Replace {REPO_ID} with the repo ID of this model.

Original Model

See HuggingFaceTB/SmolVLM-256M-Instruct for the original FP16 model.