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
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "model_max_length": 32768, | |
| "pad_token": "<|vision_pad|>", | |
| "padding_side": "left", | |
| "processor_class": "Qwen2_5_VLProcessor", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |