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", device_map="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 May 22

Commit

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1 Parent(s): 512697c

Update README.md

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@@ -1,10 +1,19 @@
 ---
 license: apache-2.0
-base_model: facebook/roberta-base
-library_name: transformers
 datasets:
 - Intel/misinformation-guard
 - ucsbnlp/liar
 language:
 - en
 - fr
@@ -25,10 +34,11 @@ tags:
 - nlp
 - news
 widget:
-- text: "The government has announced a new tax on all social media users."
-  example_title: "Policy News"
-- text: "Scientists have discovered a planet made entirely of diamond."
-  example_title: "Science Claim"
 ---
 # 1. Load the different "Subject Experts"

 ---
 license: apache-2.0
+base_model:
+- facebook/roberta-base
+- meta-llama/Llama-3.1-8B-Instruct
+library_name: transformers.js
 datasets:
 - Intel/misinformation-guard
 - ucsbnlp/liar
+- Isotonic/human_assistant_conversation
+- fever/fever
+- Holmeister/Climate-Fever-TR
+- 34data/polyglotfake-real
+- talab-ai/pi5-agricultural-iot-32day
+- brl-xfact/Eye4AllMulti
+- BeIR/scifact
 language:
 - en
 - fr
 - nlp
 - news
 widget:
+- text: The government has announced a new tax on all social media users.
+  example_title: Policy News
+- text: Scientists have discovered a planet made entirely of diamond.
+  example_title: Science Claim
+new_version: deepseek-ai/DeepSeek-V4-Pro
 ---
 # 1. Load the different "Subject Experts"