Instructions to use Gbone3176/DermL2V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Gbone3176/DermL2V with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Gbone3176/DermL2V") - Notebooks
- Google Colab
- Kaggle
Download README.md from Gbone3176/DermL2V: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/Gbone3176/DermL2V/resolve/main/README.md
- Command line
-
hf download hf://Gbone3176/DermL2V/README.md
-
curl -L -o README.md https://huggingface.co/Gbone3176/DermL2V/resolve/main/README.md
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
base_model_relation: adapter
library_name: peft
license: llama3.1
tags:
- peft
- lora
- text-embedding
- dermatology
DermL2V Adapter
DermL2V is a semantic encoder specialized for unstructured dermatology text. It maps free-form dermatology narratives and questions to dense embeddings for semantic search, retrieval, and similarity-based comparison.
This repository provides the DermL2V PEFT adapter and the Attribute-Aware Relevance Aggregation module, the model's pooling mechanism for constructing text embeddings. The underlying Llama base model is not included and must be obtained separately.
Required model chain
meta-llama/Meta-Llama-3.1-8B-Instruct
-> McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp
-> McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-supervised
-> DermL2V_adapter
The base model is subject to the Llama 3.1 Community License. Users must obtain access to the gated base model and comply with its license and use policy.
The standard adapter_model.safetensors filename is intentional: PEFT uses
this filename for automatic adapter loading.