Image-Text-to-Text
PEFT
Safetensors
English
lora
qwen2.5-vl
vision-language-model
medical
medical-vqa
chain-of-thought
reasoning
grpo
trl
conversational
Instructions to use researcher2026/OpenMedReason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use researcher2026/OpenMedReason with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-VL-7B-Instruct") model = PeftModel.from_pretrained(base_model, "researcher2026/OpenMedReason") - Notebooks
- Google Colab
- Kaggle
| base_model: unsloth/Qwen2.5-VL-7B-Instruct | |
| library_name: peft | |
| pipeline_tag: image-text-to-text | |
| license: apache-2.0 | |
| language: | |
| - en | |
| datasets: | |
| - researcher2026/OpenMedReason | |
| tags: | |
| - peft | |
| - lora | |
| - qwen2.5-vl | |
| - vision-language-model | |
| - medical | |
| - medical-vqa | |
| - chain-of-thought | |
| - reasoning | |
| - grpo | |
| - trl | |
| - base_model:adapter:unsloth/Qwen2.5-VL-7B-Instruct | |
| model-index: | |
| - name: OpenMedReason-Qwen2.5-VL-7B-GRPO-LoRA | |
| results: [] | |
| # OpenMedReason - Qwen2.5-VL-7B GRPO LoRA Adapter | |