Instructions to use Bilic/NeuralChat-finetuned-for-fraud-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bilic/NeuralChat-finetuned-for-fraud-detection with Transformers:

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

pipe = pipeline("text-generation", model="Bilic/NeuralChat-finetuned-for-fraud-detection")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bilic/NeuralChat-finetuned-for-fraud-detection")
model = AutoModelForCausalLM.from_pretrained("Bilic/NeuralChat-finetuned-for-fraud-detection", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bilic/NeuralChat-finetuned-for-fraud-detection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bilic/NeuralChat-finetuned-for-fraud-detection"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bilic/NeuralChat-finetuned-for-fraud-detection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bilic/NeuralChat-finetuned-for-fraud-detection

SGLang

How to use Bilic/NeuralChat-finetuned-for-fraud-detection 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 "Bilic/NeuralChat-finetuned-for-fraud-detection" \
    --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": "Bilic/NeuralChat-finetuned-for-fraud-detection",
		"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 "Bilic/NeuralChat-finetuned-for-fraud-detection" \
        --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": "Bilic/NeuralChat-finetuned-for-fraud-detection",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bilic/NeuralChat-finetuned-for-fraud-detection with Docker Model Runner:
```
docker model run hf.co/Bilic/NeuralChat-finetuned-for-fraud-detection
```

neural-chat-finetuned-bilic-v1

This model is a fine-tuned version of Intel/neural-chat-7b-v3-1 on our custom dataset.

Model description

This is a fine tuned version of the intel's Neuralchat model, specifically trained on a carefully curated dataset on fraud detection. We implemented a contextual based architecture to enable the model learn and be adept at understanding context within a conversation as opposed to the traditional rule based approach.

Intended uses & limitations

detecting fraudulent conversations in real-time
Giving a summary of conversations and suggestions
Understanding with high accuracy the context in a conversation to make better predictions

Training

50,000 synthetically conversations

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 250
mixed_precision_training: Native AMP

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 14

Model tree for Bilic/NeuralChat-finetuned-for-fraud-detection

Base model

mistralai/Mistral-7B-v0.1

Finetuned

Intel/neural-chat-7b-v3-1

Finetuned

(17)

this model