Instructions to use AQ-MedAI/Diver-Retriever-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AQ-MedAI/Diver-Retriever-0.6B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AQ-MedAI/Diver-Retriever-0.6B") model = AutoModelForCausalLM.from_pretrained("AQ-MedAI/Diver-Retriever-0.6B") - Notebooks
- Google Colab
- Kaggle
Quick question: is AQ-MedAI/Diver-Retriever-0.6B derived from Qwen/Qwen3-Embedding-0.6B?
Dear [Developer/Team],
I really appreciate your work on AQ-MedAI/Diver-Retriever-0.6B. I've been using it recently and it's been very helpful.
I'm planning to use it as a base, so I need to confirm its relation with Qwen/Qwen3-Embedding-0.6B:
Direct Fine-tuning: Can I assume AQ-MedAI/Diver-Retriever-0.6B is directly fine-tuned from Qwen/Qwen3-Embedding-0.6B, or not?
Inheritance: Does it strictly inherit the architecture and weights of Qwen/Qwen3-Embedding-0.6B without merging or distilling from other models?
I just want to make sure I'm using it the right way.
I really appreciate your help.
Yes, AQ-MedAI/Diver-Retriever-0.6B is directly fine-tuned from Qwen/Qwen3-Embedding-0.6B and strictly inherits its architecture and weights.