Instructions to use Jashan887/58_Fraud_Detection_Master with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jashan887/58_Fraud_Detection_Master with Transformers:

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

pipe = pipeline("text-generation", model="Jashan887/58_Fraud_Detection_Master")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jashan887/58_Fraud_Detection_Master")
model = AutoModelForCausalLM.from_pretrained("Jashan887/58_Fraud_Detection_Master")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Jashan887/58_Fraud_Detection_Master with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jashan887/58_Fraud_Detection_Master"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jashan887/58_Fraud_Detection_Master",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jashan887/58_Fraud_Detection_Master

SGLang

How to use Jashan887/58_Fraud_Detection_Master 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 "Jashan887/58_Fraud_Detection_Master" \
    --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": "Jashan887/58_Fraud_Detection_Master",
		"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 "Jashan887/58_Fraud_Detection_Master" \
        --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": "Jashan887/58_Fraud_Detection_Master",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jashan887/58_Fraud_Detection_Master with Docker Model Runner:
```
docker model run hf.co/Jashan887/58_Fraud_Detection_Master
```

58_Fraud_Detection_Master / README.md

Jashan887

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	---
	tags:
	- autotrain
	- text-generation
	- mistral
	- fine-tune
	- text-generation-inference
	- chat
	- Trained with Auto-train
	- pytorch
	widget:
	- text: 'I love AutoTrain because '
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: conversational
	---

	# Model Trained Using AutoTrain

	![LLM_IMAGE](header.jpeg)

	The mistral-7b-fraud2-finetuned Large Language Model (LLM) is a fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of synthetically generated Fraudulent transcripts datasets.

	For full details of this model please read [release blog post](https://mistral.ai/news/announcing-mistral-7b/)

	## Instruction format

	In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

	E.g.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	device = "cuda" # the device to load the model onto

	model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
	tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

	text = "<s>[INST] Below is a conversation transcript [/INST]"
	"Your credit card has been stolen, and you need to contact us to resolve the issue. We will help you protect your information and prevent further fraud.</s> "
	"[INST] Analyze the conversation and determine if it's fraudulent or legitimate. [/INST]"

	encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False)

	model_inputs = encodeds.to(device)
	model.to(device)

	generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True)
	decoded = tokenizer.batch_decode(generated_ids)
	print(decoded[0])
	```

	## Model Architecture
	This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
	- Grouped-Query Attention
	- Sliding-Window Attention
	- Byte-fallback BPE tokenizer

	## Version
	- v1

	## The Team
	- BILIC TEAM OF AI ENGINEERS