Instructions to use Feudor2/hallucination_detector_v3_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Feudor2/hallucination_detector_v3_adapter with PEFT:
```
Task type is invalid.
```

How to use Feudor2/hallucination_detector_v3_adapter with Transformers:

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

pipe = pipeline("text-generation", model="Feudor2/hallucination_detector_v3_adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Feudor2/hallucination_detector_v3_adapter")
model = AutoModelForCausalLM.from_pretrained("Feudor2/hallucination_detector_v3_adapter", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Feudor2/hallucination_detector_v3_adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Feudor2/hallucination_detector_v3_adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Feudor2/hallucination_detector_v3_adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Feudor2/hallucination_detector_v3_adapter

SGLang

How to use Feudor2/hallucination_detector_v3_adapter 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 "Feudor2/hallucination_detector_v3_adapter" \
    --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": "Feudor2/hallucination_detector_v3_adapter",
		"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 "Feudor2/hallucination_detector_v3_adapter" \
        --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": "Feudor2/hallucination_detector_v3_adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Feudor2/hallucination_detector_v3_adapter with Docker Model Runner:
```
docker model run hf.co/Feudor2/hallucination_detector_v3_adapter
```

Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string

hallucination_detector_v3

This model is a fine-tuned version of yandex/YandexGPT-5-Lite-8B-instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0420

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0592	1.0	489	0.0456
0.0455	2.0	978	0.0419
0.0397	3.0	1467	0.0420

Framework versions

PEFT 0.19.1
Transformers 5.8.1
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

F16

Model tree for Feudor2/hallucination_detector_v3_adapter

Base model

yandex/YandexGPT-5-Lite-8B-pretrain

Finetuned

yandex/YandexGPT-5-Lite-8B-instruct

Adapter

(16)

this model