LoRA Climate Model

Model Overview

Model Name: LoRA Climate
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 climate-related conversational tasks. It supports safe and efficient text generation while keeping the base model frozen and training only LoRA adapters.

Model Description

This model is intended for climate-related text generation, conversational tasks, and research purposes. It was trained using LoRA adapters on climate datasets and is compatible with Hugging Face Transformers for inference.

LoRA adapters: Low-rank adaptation for efficient fine-tuning
Base model: LLaMA2 7B
Model weights format: Safetensors
Intended use: Research, simulation, conversational AI for climate domain

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.

License

This model is released under the Apache 2.0 License, which allows:

Commercial and non-commercial use
Modification and redistribution
Requires attribution to the original author

Apache 2.0 License Details

Model Files

Included files:

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

All large binaries are tracked via Git LFS.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "AbdulSittar/llama2-lora-climate"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load base model with LoRA adapters
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
model.eval()

# Generate text
prompt = "Climate change impacts on renewable energy:"
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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