Model Details
Full Model IDISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
Pipeline / Taskimage-text-to-text
Librarygguf
Downloads (all-time)6.5M
Likes1.0K
Last Modified9/29/2026
Author / OrgISTA-DASLab
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("image-text-to-text", model="ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
ggufgsqrcoquantizationmixed-precisionist-daslabmoemultimodalvisionimage-text-to-textarxiv:2604.18556arxiv:2605.00649base_model:Qwen/Qwen3.8-Flash-Nextbase_model:quantized:Qwen/Qwen3.8-Flash-Nextlicense:apache-2.0endpoints_compatibleregion:usimatrixconversational
More image-text-to-text Models
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: image-text-to-text
This model is designed for the image-text-to-text task. Explore more models for this use case.
All image-text-to-text Models →📊 Popularity
⬇ Downloads6.5M
❤️ Community Likes1.0K
🛠️ Requirements
- →Install: pip install gguf
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.