README.md

metadata

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]

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[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)

# 데이터를 GPU로 이동(사용 가능한 경우)
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,  # 4개의 답변을 생성하도록 설정(답변 개수 설정 가능)
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id,  # EOS 토큰 ID 사용
    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()

    # "### 답변:" 문자열 이후의 텍스트만 추출
    first_answer_start_idx = decoded.find("### 답변:") + len("### 답변:")
    temp_answer = decoded[first_answer_start_idx:].strip()

    # 두 번째 "### 답변:" 문자열 이전까지의 텍스트만 추출
    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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• 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

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Software

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Citation [optional]