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

f866ab5

verified ·

1 Parent(s): fc10272

configure model card metadata

Browse files

Initialized model card with core metadata for digital literacy and fact-checking. Configured the text-classification pipeline and added relevant tags (misinformation, media literacy) to improve discoverability. Defined transformers as the primary library and linked the base model architecture.
![WhatsApp Image 2026-05-21 at 08.43.49](https://cdn-uploads.huggingface.co/production/uploads/69ce92e3974332e3e056410c/ancPyXhYCBhy_Yvy7eyDi.jpeg)

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Files changed (1) hide show

README.md +30 -1

README.md CHANGED Viewed

@@ -1,3 +1,32 @@
 ---
 license: apache-2.0
----

 ---
 license: apache-2.0
+datasets:
+- Intel/misinformation-guard
+- ucsbnlp/liar
+language:
+- en
+- tw
+- fr
+- ar
+- ha
+- es
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+base_model:
+- FacebookAI/roberta-base
+- meta-llama/Llama-3.2-3B-Instruct
+- google/gemma-4-31B-it
+- sentence-transformers/all-MiniLM-L6-v2
+new_version: deepseek-ai/DeepSeek-V4-Pro
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- fact checking
+- misinformation
+- digital literacy
+- fake news
+- media literacy
+---