ko
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
krx
finance
sft
trl
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Google의 Gemma 2 2b 모델을 금융 도메인 데이터셋을 정재한 데이터셋을 Continual Learning을 하여 학습 한 모델에 금융 도메인 Insturction 데이터 셋으로 학습 시킨 모델입니다.
Usage
Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
pip install -U transformers
Then, copy the snippet from the section that is relevant for your usecase.
Running with the pipeline API
python
1import torch
2from transformers import pipeline
34pipe = pipeline(5"text-generation",6 model="miner41612/gemma-2-2b-finance-it-v1",7 model_kwargs={"torch_dtype": torch.bfloat16},8 device="cuda",# replace with "mps" to run on a Mac device9)1011messages =[12{"role":"user","content":"원가상환제도란?"},13]1415outputs = pipe(messages, max_new_tokens=256)16assistant_response = outputs[0]["generated_text"][-1]["content"].strip()17print(assistant_response)
Torch compile is a method for speeding-up the
inference of PyTorch modules. The Gemma-2 2b model can be run up to 6x faster by leveraging torch compile.
Note that two warm-up steps are required before the full inference speed is realised:
Data used for model training and how the data was processed.
Ethics and Safety
Ethics and safety evaluation approach and results.
Dangerous Capability Evaluations
Evaluation Approach
We evaluated a range of dangerous capabilities:
Offensive cybersecurity: To assess the model's potential for misuse in
cybersecurity contexts, we utilized both publicly available
Capture-the-Flag (CTF) platforms like InterCode-CTF and Hack the Box, as
well as internally developed CTF challenges. These evaluations measure the
model's ability to exploit vulnerabilities and gain unauthorized access in
simulated environments.
Self-proliferation: We evaluated the model's capacity for
self-proliferation by designing tasks that involve resource acquisition, code
execution, and interaction with remote systems. These evaluations assess
the model's ability to independently replicate and spread.
Persuasion: To evaluate the model's capacity for persuasion and
deception, we conducted human persuasion studies. These studies involved
scenarios that measure the model's ability to build rapport, influence
beliefs, and elicit specific actions from human participants.
Usage and Limitations
These models have certain limitations that users should be aware of.
Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
Content Creation and Communication
Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
Research and Education
Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
Limitations
Training Data
The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
The scope of the training dataset determines the subject areas the model can
handle effectively.
Context and Task Complexity
LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
Language Ambiguity and Nuance
Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
Factual Accuracy
LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
Common Sense
LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
Bias and Fairness
LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
Misinformation and Misuse
LLMs can be misused to generate text that is false, misleading, or harmful.
Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
Transparency and Accountability:
This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
