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
```

ARISCOT commited on 18 days ago

Commit

2792b59

verified ·

1 Parent(s): dc16cd5

Add official NOTICE file

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+# 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.