--- license: apache-2.0 base_model: Qwen/Qwen1.5-0.5B-Chat tags: - trl - orpo model-index: - name: Qwen-Orpo-v1 results: [] --- ## 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: ```python 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) ```