FINGU-AI
/

FinguAI-Chat-v1

@@ -1,201 +1,79 @@
 ---
-library_name: transformers
-tags:
-- trl
-- sft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: apache-2.0
+language:
+- en
+- ko
+- ja
 ---
+## FINGU-AI/QwenllmFi
+### Overview
+The FINGU-AI/QwenllmFi, model offers a specialized curriculum tailored to English, Korean, and Japanese 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/QwenllmFi
+- **Description**: FINGU-AI/QwenllmFi model trained on various languages, including English, Korean, and Japanese.
+- **Checkpoint**: FINGU-AI/QwenllmFi
+- **Author**:
+- **License**: Apache-2.0
+### How to Use
+To use the FINGU-AI/QwenllmFi 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
+#!pip install 'transformers>=4.39.0'
+#!pip install -U flash-attn
+#!pip install -q -U git+https://github.com/huggingface/accelerate.
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig,TextStreamer
+streamer = TextStreamer(tokenizer)
+model_id = 'FINGU-AI/QwenllmFi'
+#config = AutoConfig.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype= torch.bfloat16,)
+model.to('cuda')
+tokenizer = AutoTokenizer.from_pretrained(model_id,)
+streamer = TextStreamer(tokenizer)
+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,
+    #'prompt_lookup_num_tokens':10,
+    'do_sample': True,
+    'temperature': 0.7,
+    'top_p': 0.9,
+    'top_k': 50,
+    #'bos_token_id': tokenizer.bos_token_id,
+    'eos_token_id': tokenizer.eos_token_id,
+}
+outputs = model.generate(tokenized_chat, **generation_params, streamer=streamer)
+decoded_outputs = tokenizer.batch_decode(outputs)
+'''
+<|im_start|>user
+what are the best approch to prevent loss? provide some tips and suggestions.<|im_end|>
+<|im_start|>assistant
+To avoid losses, it's essential to maintain discipline, set realistic goals, and adhere to predetermined rules for trading. Diversification is key as it spreads investments across different sectors and asset classes to reduce overall risk. Regularly reviewing and rebalancing positions can also ensure alignment with investment objectives. Additionally, staying informed about market trends and economic indicators can provide opportunities for long-term capital preservation. It's also important to stay patient and avoid emotional decision-making, as emotions often cloud judgment. If you encounter significant losses, consider using stop-loss orders to limit your losses. Staying disciplined and focusing on long-term objectives can help protect your investment portfolio from permanent damage.
+'''
+```