Instructions to use ARISCOT/Digital_Literacy_Fact_Checker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ARISCOT/Digital_Literacy_Fact_Checker with Transformers:

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

pipe = pipeline("text-generation", model="ARISCOT/Digital_Literacy_Fact_Checker")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ARISCOT/Digital_Literacy_Fact_Checker", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ARISCOT/Digital_Literacy_Fact_Checker with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ARISCOT/Digital_Literacy_Fact_Checker"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ARISCOT/Digital_Literacy_Fact_Checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ARISCOT/Digital_Literacy_Fact_Checker

SGLang

How to use ARISCOT/Digital_Literacy_Fact_Checker 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 "ARISCOT/Digital_Literacy_Fact_Checker" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ARISCOT/Digital_Literacy_Fact_Checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ARISCOT/Digital_Literacy_Fact_Checker" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ARISCOT/Digital_Literacy_Fact_Checker",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ARISCOT/Digital_Literacy_Fact_Checker with Docker Model Runner:
```
docker model run hf.co/ARISCOT/Digital_Literacy_Fact_Checker
```

Digital_Literacy_Fact_Checker / README.md

ARISCOT

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	---
	language:
	- en
	- fr
	- tw
	- ha
	- es
	license: apache-2.0
	tags:
	- fact-checking
	- social-media
	- politics
	- health
	- science
	datasets:
	- mteb/FEVER_test_top_250_only_w_correct-v2
	- ucsbnlp/liar
	- NunoBatista/PHEME-Misinformation-Graphs
	- dwadden/healthver_entailment
	- rabuahmad/climatecheck
	- talab-ai/pi5-agricultural-iot-32day
	- RASSAISAID/finqa-deepseek-prompts
	metrics:
	- accuracy
	- recall
	- f1
	- precision
	base_model:
	- FacebookAI/roberta-base
	- meta-llama/Llama-3.2-1B-Instruct
	new_version: deepseek-ai/DeepSeek-V4-Pro-Base
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Digital Literacy Fact Checker
	This model is designed to classify misinformation across social media,
	politics, health, science, religion, and agriculture.
	# NOTICE

	Digital Literacy & Fact-Checker AI (Ghana Edition)
	Copyright 2026 George Asomaning Peprah

	This project is licensed under the Apache License, Version 2.0 (the "License");
	you may not use this file except in compliance with the License.
	You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.

	---
	### ATTRIBUTION
	This model was developed by George Asomaning Peprah as part of a mission to
	improve digital literacy and combat misinformation in Ghana and West Africa.

	The model incorporates weights and/or data inspired by or derived from:
	- LIAR Dataset
	- FEVER Dataset
	- Misinformation-Guard
	- Custom Ghanaian Digital Literacy benchmarks

	Any redistribution of this model or derivative works MUST include this
	NOTICE file in its entirety.