README.md

---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---


# Model Details
Model Developers: Sogang University SGEconFinlab

### Model Description

This model is a language model specialized in economics and finance. This was learned with various economic/finance-related data.
The data sources are listed below, and we are not releasing the data we trained on because it was used for research/policy purposes. 
If you wish to use the original data rather than our training data, please contact the original author directly for permission to use it.

- **Developed by:** Sogang University SGEconFinlab
- **Language(s) (NLP):** Ko/En
- **License:** apache-2.0
- **Base Model:** yanolja/KoSOLAR-10.7B-v0.2

## 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]


## How to Get Started with the Model


    peft_model_id = "SGEcon/KoSOLAR-10.7B-v0.2_fin_v4"
    config = PeftConfig.from_pretrained(peft_model_id)
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16
    )
    model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, quantization_config=bnb_config, device_map={"":0})
    model = PeftModel.from_pretrained(model, peft_model_id)
    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
    model.eval()


    import re
    def gen(x):
        inputs = tokenizer(f"### 질문: {x}\n\n### 답변:", return_tensors='pt', return_token_type_ids=False)
    
        # Move data to GPU (if available)
        inputs = {k: v.to(device="cuda" if torch.cuda.is_available() else "cpu") for k, v in inputs.items()}

        gened = model.generate(
            **inputs,
            max_new_tokens=256,
            early_stopping=True,
            num_return_sequences=4,  
            do_sample=True,
            eos_token_id=tokenizer.eos_token_id,  
            temperature=0.9,
            top_p=0.8,
            top_k=50
        )
    
        complete_answers = []
        for gen_seq in gened:
            decoded = tokenizer.decode(gen_seq, skip_special_tokens=True).strip()

            # Extract only the text after the string "### 답변:"
            first_answer_start_idx = decoded.find("### 답변:") + len("### 답변:")
            temp_answer = decoded[first_answer_start_idx:].strip()

            # Extract only text up to the second "### 답변:" string
            second_answer_start_idx = temp_answer.find("### 답변:")
            if second_answer_start_idx != -1:
                complete_answer = temp_answer[:second_answer_start_idx].strip()
            else:
                complete_answer = temp_answer  # 두 번째 "### 답변:"이 없는 경우 전체 답변 반환
        
            complete_answers.append(complete_answer)
    
        return complete_answers

    
## 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


## 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. -->