Instructions to use future7/CogniDet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use future7/CogniDet with Transformers:

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

pipe = pipeline("text-generation", model="future7/CogniDet")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("future7/CogniDet")
model = AutoModelForCausalLM.from_pretrained("future7/CogniDet")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use future7/CogniDet with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "future7/CogniDet"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "future7/CogniDet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/future7/CogniDet

SGLang

How to use future7/CogniDet 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 "future7/CogniDet" \
    --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": "future7/CogniDet",
		"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 "future7/CogniDet" \
        --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": "future7/CogniDet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use future7/CogniDet with Docker Model Runner:
```
docker model run hf.co/future7/CogniDet
```

nielsr HF Staff commited on Jun 28, 2025

Commit

2c8c0ba

verified ·

1 Parent(s): fa3a3c1

Improve model card: Add paper link and refine description

Browse files

This PR adds a link to the paper and refines the description of the CogniDet model. It also ensures the model card includes all necessary information, such as usage examples, training data details, limitations, and citation information.

Files changed (1) hide show

README.md +11 -10

README.md CHANGED Viewed

@@ -1,4 +1,11 @@
 ---
 library_name: transformers
 pipeline_tag: text-generation
 tags:
@@ -7,19 +14,11 @@ tags:
 - RAG evaluation
 - cognitive statements
 - factual consistency
-datasets:
-- future7/CogniBench
-- future7/CogniBench-L
-language:
-- en
-base_model:
-- meta-llama/Meta-Llama-3-8B
 ---
 # CogniDet: Cognitive Faithfulness Detector for LLMs
-**CogniDet** is a state-of-the-art model for detecting **both factual and cognitive hallucinations** in Large Language Model (LLM) outputs. Developed as part of the [CogniBench](https://github.com/FUTUREEEEEE/CogniBench) framework, it specifically addresses the challenge of evaluating inference-based statements beyond simple fact regurgitation.
 ## Key Features ✨
 1. **Dual Detection Capability**
@@ -55,7 +54,9 @@ model = AutoModelForCausalLM.from_pretrained(model_id)
 def detect_hallucinations(context, response):
     inputs = tokenizer(
-        f"CONTEXT: {context}\nRESPONSE: {response}\nHALLUCINATIONS:",
         return_tensors="pt"
     )
     outputs = model.generate(**inputs, max_new_tokens=100)

 ---
+base_model:
+- meta-llama/Meta-Llama-3-8B
+datasets:
+- future7/CogniBench
+- future7/CogniBench-L
+language:
+- en
 library_name: transformers
 pipeline_tag: text-generation
 tags:
 - RAG evaluation
 - cognitive statements
 - factual consistency
 ---
 # CogniDet: Cognitive Faithfulness Detector for LLMs
+**CogniDet** is a state-of-the-art model for detecting **both factual and cognitive hallucinations** in Large Language Model (LLM) outputs. Developed as part of the [CogniBench](https://github.com/FUTUREEEEEE/CogniBench) framework, it specifically addresses the challenge of evaluating inference-based statements beyond simple fact regurgitation. The model is presented in the paper [CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models](https://huggingface.co/papers/2505.20767).
 ## Key Features ✨
 1. **Dual Detection Capability**
 def detect_hallucinations(context, response):
     inputs = tokenizer(
+        f"CONTEXT: {context}
+RESPONSE: {response}
+HALLUCINATIONS:",
         return_tensors="pt"
     )
     outputs = model.generate(**inputs, max_new_tokens=100)