Update README.md

README.md

@@ -8,3 +8,67 @@ 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 supervised 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)
+
+```