miner41612/gemma-2-2b-finance-it

Gemma 2 Finance model card

Model Page: Gemma

Terms of Use: [Terms][terms]

Authors: miner41612

Model Information

입력 및 출력에 대한 요약 설명과 간략한 정의입니다.

Description

Google의 Gemma 2 2b 모델을 금융 도메인 데이터셋을 정재한 데이터셋을 Continual Learning을 하여 학습 한 모델에 금융 도메인 Insturction 데이터 셋으로 학습 시킨 모델입낟.

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

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="miner41612/gemma-2-2b-finance-it",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",  # replace with "mps" to run on a Mac device
)

messages = [
    {"role": "user", "content": "원가상환제도란?"},
]

outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)

Running the model on a single / multi GPU

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("miner41612/gemma-2-2b-finance-it")
model = AutoModelForCausalLM.from_pretrained(
    "miner41612/gemma-2-2b-finance-it",
    device_map="auto",
)

input_text = "원가상환제도란?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

You can ensure the correct chat template is applied by using tokenizer.apply_chat_template as follows:

messages = [
    {"role": "user", "content": "원가상환제도란?"},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Quantized Versions through bitsandbytes

Using 8-bit precision (int8)

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

tokenizer = AutoTokenizer.from_pretrained("miner41612/gemma-2-2b-finance-it")
model = AutoModelForCausalLM.from_pretrained(
    "miner41612/gemma-2-2b-finance-it",
    quantization_config=quantization_config,
)

input_text = "원가상환제도란?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Using 4-bit precision

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

tokenizer = AutoTokenizer.from_pretrained("miner41612/gemma-2-2b-finance-it")
model = AutoModelForCausalLM.from_pretrained(
    "miner41612/gemma-2-2b-finance-it",
    quantization_config=quantization_config,
)

input_text = "원가상환제도란?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Advanced Usage

Torch compile

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:

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

from transformers import AutoTokenizer, Gemma2ForCausalLM
from transformers.cache_utils import HybridCache
import torch

torch.set_float32_matmul_precision("high")

# load the model + tokenizer
tokenizer = AutoTokenizer.from_pretrained("miner41612/gemma-2-2b-finance-it")
model = Gemma2ForCausalLM.from_pretrained("miner41612/gemma-2-2b-finance-it", torch_dtype=torch.bfloat16)
model.to("cuda")

# apply the torch compile transformation
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

# pre-process inputs
input_text = "원가상환제도란? "
model_inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
prompt_length = model_inputs.input_ids.shape[1]

# set-up k/v cache
past_key_values = HybridCache(
    config=model.config,
    max_batch_size=1,
    max_cache_len=model.config.max_position_embeddings,
    device=model.device,
    dtype=model.dtype
)

# enable passing kv cache to generate
model._supports_cache_class = True
model.generation_config.cache_implementation = None

# two warm-up steps
for idx in range(2):
    outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
    past_key_values.reset()

# fast run
outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For more details, refer to the Transformers documentation.

Inputs and outputs

• Input: Text string, such as a question, a prompt, or a document to be summarized.
• Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.

Citation

@article{gemma_2024,
    title={Gemma},
    url={https://www.kaggle.com/m/3301},
    DOI={10.34740/KAGGLE/M/3301},
    publisher={Kaggle},
    author={Gemma Team},
    year={2024}
}

Model Data

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.