LoRA COVID Model

Model Overview

Model Name: LoRA COVID
Developed by: Abdul Sittar
Model Type: Text Generation (PEFT, LoRA)
Frameworks: Hugging Face Transformers, PEFT, Safetensors
Languages: English
License: Apache 2.0

This model is a LoRA-finetuned version of LLaMA2 7B, adapted for COVID-related conversational tasks.

Dataset Used

This model was trained using the Social Graph Inference Reddit dataset:

DOI / Link: https://zenodo.org/records/18082502

Authors/Creators:

Sittar, Abdul
Guček, Alenka
Češnovar, Miha

Description:
A large-scale, empirically grounded dataset from Reddit to support agent-based social simulations. Includes:

33 technology-focused agents
14 climate-focused agents
7 COVID-related agents
Each domain includes over one million posts and comments

The dataset defines agent categories, derives inter-agent relationships, and builds directed, weighted networks reflecting real user interactions.

Model Files

adapter_model.safetensors – LoRA adapter weights
tokenizer.model – Tokenizer model
tokenizer.json – Tokenizer JSON config
adapter_config.json – LoRA configuration (moved to configs/)
tokenizer_config.json – Tokenizer configuration (moved to configs/)
special_tokens_map.json – Special tokens mapping (moved to configs/)
chat_template.jinja – Conversation template for inference
README.md – Model card and instructions

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "AbdulSittar/llama2-lora-covid"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("configs")

# Load model
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
model.eval()

prompt = "Latest COVID-19 variants and vaccines:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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