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Model: pfnet/nekomata-7b-pfn-qfin Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_name: tongyi-qianwen-license
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license_link: LICENSE
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language:
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- en
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- ja
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library_name: transformers
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pipeline_tag: text-generation
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---
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# nekomata-7b-pfn-qfin
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## Model Description
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nekomata-7b-pfn-qfin is a fine-tuned model based on [rinna/nekomata-7b](https://huggingface.co/rinna/nekomata-7b/tree/main).
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This is the base model, which is good at generating continuous sentences for finance.
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nekomata-7b-pfn-qfin is fine-tuned on 370M tokens from multiple special datasets generated by Preferred Networks, which is clear to use for commercial usage.
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The fine-tuned were carried out at a 2048 context length.
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This model is released under [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT).
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The research article is available on [arXiv](https://arxiv.org/abs/2404.10555).
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# Benchmarking
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The benchmark score is obtained using [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness)
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For the benchmark, 0-shot and default prompts are used.
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```
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| Task |Metric| nekomaba-7b | Ours |
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|----------------|------|------|---|------|------|---|------|
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|chabsa |f1 |0.8134| | |0.8127| | |
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|cma_basics |acc |0.3158|± |0.0764|0.3684|± |0.0793|
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|cpa_audit |acc |0.2085|± |0.0203|0.1809|± |0.0193|
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|fp2 |acc |0.2484|± |0.0198|0.2674|± |0.0203|
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|security_sales_1|acc |0.4912|± |0.0668|0.5088|± |0.0668|
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|----------------|------|------|---|------|------|---|------|
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|OVER ALL | |0.4155 |0.4276 |
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```
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## Usage
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Install the required libraries as follows:
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```sh
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>>> python -m pip install numpy sentencepiece torch transformers accelerate transformers_stream_generator tiktoken einops
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```
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Execute the following python code:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("pfnet/nekomata-7b-pfn-qfin", trust_remote_code=True)
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# Use GPU with bf16 (recommended for supported devices)
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, bf16=True)
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# Use GPU with fp16
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, fp16=True)
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# Use GPU with fp32
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True, fp32=True)
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# Use CPU
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# model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="cpu", trust_remote_code=True)
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# Automatically select device and precision
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model = AutoModelForCausalLM.from_pretrained("pfnet/nekomata-7b-pfn-qfin", device_map="auto", trust_remote_code=True)
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text = "日本銀行は"
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input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
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with torch.no_grad():
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generated_tokens = model.generate(
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inputs=input_ids,
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max_new_tokens=32,
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do_sample=True,
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temperature=1.0,
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repetition_penalty=1.1
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)[0]
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generated_text = tokenizer.decode(generated_tokens)
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print(generated_text)
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# 日本銀行は、2016年9月に「長短金利操作付き量的・質的金融緩和」を導入し、長期国
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```
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## Model Details
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- Model size: 7b
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- Fine-tuned tokens: 370M tokens (Japanese: 300M tokens, English: 13M tokens, Digits: 14M tokens)
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- Context length: 2048
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- Developed by: Preferred Networks, Inc
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- Model type: Causal decoder-only
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- Language(s): Japanese and English
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- License: [Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)
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## Bias, Risks, and Limitations
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nekomata-7b-pfn-qfin is a new technology that carries risks with use.
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Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios.
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For these reasons, as with all LLMs, nekomata-7b-pfn-qfin’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts.
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This model is not designed for legal, tax, investment, financial, or other advice.
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Therefore, before deploying any applications of nekomata-7b-pfn-qfin, developers should perform safety testing and tuning tailored to their specific applications of the model.
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## How to cite
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```
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@misc{hirano2024,
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title={Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training},
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author={Masanori Hirano and Kentaro Imajo},
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year={2024},
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eprint={2404.10555},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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## Contributors
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Preferred Networks, Inc.
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- Masanori Hirano
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- Kentaro Imajo
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# License
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[Tongyi Qianwen LICENSE AGREEMENT](https://github.com/QwenLM/Qwen/blob/e8e15962d897714944773cca57fa2e460a3655e8/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)
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