Instructions to use AbdulSittar/llama2-lora-covid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use AbdulSittar/llama2-lora-covid with PEFT:
```
Task type is invalid.
```

How to use AbdulSittar/llama2-lora-covid with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AbdulSittar/llama2-lora-covid")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AbdulSittar/llama2-lora-covid", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbdulSittar/llama2-lora-covid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbdulSittar/llama2-lora-covid"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbdulSittar/llama2-lora-covid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AbdulSittar/llama2-lora-covid

SGLang

How to use AbdulSittar/llama2-lora-covid with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AbdulSittar/llama2-lora-covid" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbdulSittar/llama2-lora-covid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AbdulSittar/llama2-lora-covid" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbdulSittar/llama2-lora-covid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AbdulSittar/llama2-lora-covid with Docker Model Runner:
```
docker model run hf.co/AbdulSittar/llama2-lora-covid
```

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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