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

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

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbdulSittar/llama2-lora-technology"
# 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-technology",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use AbdulSittar/llama2-lora-technology 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-technology" \
    --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-technology",
		"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-technology" \
        --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-technology",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

LoRA Technology Model

Model Overview

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

Model Card

Model Description

This model is intended for technology-related text generation, conversational tasks, and research purposes. It was trained using LoRA adapters on technology domain 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 technology domain

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

The following files are included in this repository:

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.

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.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "AbdulSittar/llama2-lora-technology"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load base model with LoRA adapters
tokenizer = AutoTokenizer.from_pretrained(os.path.join(repo_path, "configs"))
model = AutoModelForCausalLM.from_pretrained(repo_path, device_map="auto")

model.eval()

# Generate text
prompt = "Latest trends in AI and machine learning:"
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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