Instructions to use FINGU-AI/Qwen-Orpo-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINGU-AI/Qwen-Orpo-v1 with Transformers:

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

pipe = pipeline("text-generation", model="FINGU-AI/Qwen-Orpo-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINGU-AI/Qwen-Orpo-v1")
model = AutoModelForCausalLM.from_pretrained("FINGU-AI/Qwen-Orpo-v1")
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

vLLM

How to use FINGU-AI/Qwen-Orpo-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FINGU-AI/Qwen-Orpo-v1

SGLang

How to use FINGU-AI/Qwen-Orpo-v1 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 "FINGU-AI/Qwen-Orpo-v1" \
    --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": "FINGU-AI/Qwen-Orpo-v1",
		"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 "FINGU-AI/Qwen-Orpo-v1" \
        --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": "FINGU-AI/Qwen-Orpo-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINGU-AI/Qwen-Orpo-v1 with Docker Model Runner:
```
docker model run hf.co/FINGU-AI/Qwen-Orpo-v1
```

FINGU-AI/Qwen-Orpo-v1

Overview

The FINGU-AI/Qwen-Orpo-v1 model offers a specialized curriculum tailored to English, speakers interested in finance, investment, and legal frameworks. It aims to enhance language proficiency while providing insights into global finance markets and regulatory landscapes.

Key Features

Global Perspective: Explores diverse financial markets and regulations across English, Korean, and Japanese contexts.
Language Proficiency: Enhances language skills in English, Korean, and Japanese for effective communication in finance and legal domains.
Career Advancement: Equips learners with knowledge and skills for roles in investment banking, corporate finance, asset management, and regulatory compliance.

Model Information

Model Name: FINGU-AI/Qwen-Orpo-v1
Description: FINGU-AI/Qwen-Orpo-v1 model trained on various languages, including English.
Checkpoint: FINGU-AI/Qwen-Orpo-v1
Author: Grinda AI Inc.
License: Apache-2.0

Training Details

Fine-Tuning: The model was fine-tuned on the base model Qwen/Qwen1.5-0.5B-Chat through ORPO fine-tuning using the TrL Library and Transformer.
Dataset: The fine-tuning dataset consisted of 28k training samples.

How to Use

To use the FINGU-AI/Qwen-Orpo-v1 model, you can utilize the Hugging Face Transformers library. Here's a Python code snippet demonstrating how to load the model and generate predictions:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig,TextStreamer


model_id = 'FINGU-AI/Qwen-Orpo-v1'
model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype= torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer)
model.to('cuda')



messages = [
    {"role": "system","content": " you are as a finance specialist, help the user and provide accurat information."},
    {"role": "user", "content": " what are the best approch to prevent loss?"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

generation_params = {
    'max_new_tokens': 1000,
    'use_cache': True,
    'do_sample': True,
    'temperature': 0.7,
    'top_p': 0.9,
    'top_k': 50,
}

outputs = model.generate(tokenized_chat, **generation_params, streamer=streamer)
decoded_outputs = tokenizer.batch_decode(outputs)

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Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for FINGU-AI/Qwen-Orpo-v1

Base model

Qwen/Qwen1.5-0.5B-Chat

Finetuned

(19)

this model

FINGU-AI
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Qwen-Orpo-v1