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149
LICENSE
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149
LICENSE
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Llama-2-Ko 7b MIT License under LLAMA 2 COMMUNITY LICENSE AGREEMENT
|
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|
||||
Copyright (c) 2023 L. Junbum (Beomi)
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|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
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copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
||||
---
|
||||
|
||||
LLAMA 2 COMMUNITY LICENSE AGREEMENT
|
||||
Llama 2 Version Release Date: July 18, 2023
|
||||
|
||||
"Agreement" means the terms and conditions for use, reproduction, distribution and
|
||||
modification of the Llama Materials set forth herein.
|
||||
|
||||
"Documentation" means the specifications, manuals and documentation
|
||||
accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
|
||||
libraries/llama-downloads/.
|
||||
|
||||
"Licensee" or "you" means you, or your employer or any other person or entity (if
|
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you are entering into this Agreement on such person or entity's behalf), of the age
|
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required under applicable laws, rules or regulations to provide legal consent and that
|
||||
has legal authority to bind your employer or such other person or entity if you are
|
||||
entering in this Agreement on their behalf.
|
||||
|
||||
"Llama 2" means the foundational large language models and software and
|
||||
algorithms, including machine-learning model code, trained model weights,
|
||||
inference-enabling code, training-enabling code, fine-tuning enabling code and other
|
||||
elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
|
||||
libraries/llama-downloads/.
|
||||
|
||||
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
|
||||
Documentation (and any portion thereof) made available under this Agreement.
|
||||
|
||||
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you
|
||||
are an entity, your principal place of business is in the EEA or Switzerland) and Meta
|
||||
Platforms, Inc. (if you are located outside of the EEA or Switzerland).
|
||||
|
||||
By clicking "I Accept" below or by using or distributing any portion or element of the
|
||||
Llama Materials, you agree to be bound by this Agreement.
|
||||
|
||||
1. License Rights and Redistribution.
|
||||
|
||||
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
|
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transferable and royalty-free limited license under Meta's intellectual property or
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other rights owned by Meta embodied in the Llama Materials to use, reproduce,
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distribute, copy, create derivative works of, and make modifications to the Llama
|
||||
Materials.
|
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|
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b. Redistribution and Use.
|
||||
|
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i. If you distribute or make the Llama Materials, or any derivative works
|
||||
thereof, available to a third party, you shall provide a copy of this Agreement to such
|
||||
third party.
|
||||
ii. If you receive Llama Materials, or any derivative works thereof, from
|
||||
a Licensee as part of an integrated end user product, then Section 2 of this
|
||||
Agreement will not apply to you.
|
||||
|
||||
iii. You must retain in all copies of the Llama Materials that you
|
||||
distribute the following attribution notice within a "Notice" text file distributed as a
|
||||
part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License,
|
||||
Copyright (c) Meta Platforms, Inc. All Rights Reserved."
|
||||
|
||||
iv. Your use of the Llama Materials must comply with applicable laws
|
||||
and regulations (including trade compliance laws and regulations) and adhere to the
|
||||
Acceptable Use Policy for the Llama Materials (available at
|
||||
https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into
|
||||
this Agreement.
|
||||
|
||||
v. You will not use the Llama Materials or any output or results of the
|
||||
Llama Materials to improve any other large language model (excluding Llama 2 or
|
||||
derivative works thereof).
|
||||
|
||||
2. Additional Commercial Terms. If, on the Llama 2 version release date, the
|
||||
monthly active users of the products or services made available by or for Licensee,
|
||||
or Licensee's affiliates, is greater than 700 million monthly active users in the
|
||||
preceding calendar month, you must request a license from Meta, which Meta may
|
||||
grant to you in its sole discretion, and you are not authorized to exercise any of the
|
||||
rights under this Agreement unless or until Meta otherwise expressly grants you
|
||||
such rights.
|
||||
|
||||
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE
|
||||
LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE
|
||||
PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
|
||||
EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY
|
||||
WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR
|
||||
FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE
|
||||
FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING
|
||||
THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR
|
||||
USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
|
||||
|
||||
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE
|
||||
LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT,
|
||||
NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS
|
||||
AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL,
|
||||
CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN
|
||||
IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
|
||||
ANY OF THE FOREGOING.
|
||||
|
||||
5. Intellectual Property.
|
||||
|
||||
a. No trademark licenses are granted under this Agreement, and in
|
||||
connection with the Llama Materials, neither Meta nor Licensee may use any name
|
||||
or mark owned by or associated with the other or any of its affiliates, except as
|
||||
required for reasonable and customary use in describing and redistributing the
|
||||
Llama Materials.
|
||||
|
||||
b. Subject to Meta's ownership of Llama Materials and derivatives made by or
|
||||
for Meta, with respect to any derivative works and modifications of the Llama
|
||||
Materials that are made by you, as between you and Meta, you are and will be the
|
||||
owner of such derivative works and modifications.
|
||||
|
||||
c. If you institute litigation or other proceedings against Meta or any entity
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama
|
||||
Materials or Llama 2 outputs or results, or any portion of any of the foregoing,
|
||||
constitutes infringement of intellectual property or other rights owned or licensable
|
||||
by you, then any licenses granted to you under this Agreement shall terminate as of
|
||||
the date such litigation or claim is filed or instituted. You will indemnify and hold
|
||||
harmless Meta from and against any claim by any third party arising out of or related
|
||||
to your use or distribution of the Llama Materials.
|
||||
|
||||
6. Term and Termination. The term of this Agreement will commence upon your
|
||||
acceptance of this Agreement or access to the Llama Materials and will continue in
|
||||
full force and effect until terminated in accordance with the terms and conditions
|
||||
herein. Meta may terminate this Agreement if you are in breach of any term or
|
||||
condition of this Agreement. Upon termination of this Agreement, you shall delete
|
||||
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the
|
||||
termination of this Agreement.
|
||||
|
||||
7. Governing Law and Jurisdiction. This Agreement will be governed and
|
||||
construed under the laws of the State of California without regard to choice of law
|
||||
principles, and the UN Convention on Contracts for the International Sale of Goods
|
||||
does not apply to this Agreement. The courts of California shall have exclusive
|
||||
jurisdiction of any dispute arising out of this Agreement.
|
||||
243
README.md
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README.md
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---
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language:
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- en
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- ko
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pipeline_tag: text-generation
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inference: false
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tags:
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- facebook
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- meta
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- pytorch
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- llama
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- llama-2
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- kollama
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- llama-2-ko
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- llama-2-ko-chat
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- legal-llama
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- law-llama
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- legal-gpt
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- law-gpt
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---
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<img src=https://github.com/taemin6697/Paper_Review/assets/96530685/9f94505c-4fda-41ae-9a67-1e4c96c501cc style="max-width: 500px; width: 100%" />
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Llama-2-Ko-7b-Chat은 [kfkas/Llama-2-ko-7b-Chat](https://huggingface.co/kfkas/Llama-2-ko-7b-Chat)를 토대로 만들어졌습니다. 학습 데이터는 자체 법률 질의 응답 데이터를 통해 학습하였습니다.
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## Model Details
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**Backbone Model** kfkas/Llama-2-ko-7b-Chat
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**Model Developers** Boostcamp AI Tech Team YoonSeul
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**data** leagl data
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**prompts** kullm
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**Input** Models input text only.
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**Output** Models generate text only.
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|
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---
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| Model | 질문 : 아빠가 돌아가시고 나서 한 정승인과 상속 포기 판결문을 받았는데요. 그러고 나서 근로 건설공제회에서 전화가 왔는데요. 퇴직금 공제가 있다고 신청하여서 찾아가라고 하였더라고요. 근데 이게 상속인의 고유재산이 다 아니다 하여 법원에 재산으로 신고하여야 한다고 안 해도 된다고 합니다. 정확하게 알고 싶습니다. 상속인의 고유재산으로 법원에 재산 신고하여야 하나요? 아니면 안 해도 되나요? |
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|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Llama-2-ko-7b-Chat | `[안녕하세요! 상속인 고유 재산이 아니더라도 근로건설공제회로부터 퇴직금을 지급받기 위해 법원에 재산을 신고해야 할 수 있습니다. 하지만 구체적인 상황은 상속인이 속한 국가의 법률에 따라 달라질 수 있으므로, 해당 법률 전문가에게 문의하거나 확인하는 것이 좋습니다. 상속인이 상속 재산을 신고하지 않고도 퇴직금을 받을 수 있는 경우도 있지만, 경우에 따라 추가적인 절차가 필요할 수도 있습니다. 상속인은 상속 재산의 범위가 불분명하거나 상속 재산이 전혀 없을 수 있는 등 다양한 상황에 직면할 수 있기 때문에, 관련 법률 전문가의 조언을 구하는 것이 중요합니다.]` |
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| kfkas/Legal-Llama-2-ko-7b-Chat | `[상속인의 고유 재산은 상속인에게 귀속되는 상속 재산입니다. 상속인이 상속 재산에 대한 권리와 의무를 행사할 수 있습니다. 상속 재산에는 상속인 고유 재산과 일반 상속 재산이 포함됩니다. 상속인은 상속 재산의 범위 내에서 상속 재산을 관리하고 처분할 수 있으며, 상속 재산 중 일부를 다른 상속인에게 증여할 수도 있습니다.그러나 상속 재산 중에서 상속인이 고유 재산으로 취득한 재산이 있는 경우, 상속인은 그 재산을 상속 재산에서 제외속세가 과세됩니다.따라서 상속 재산 중에 상속인이 소유한 고유 재산을 제외하고 상속 재산을 계산하여 상속세 신고를 해야 하며, 상속세 신고 시에는 상속 재산 목록을 작성하여 제출하여야 합니다. 또한, 상속세는 상속 개시일(사망일)로부터 6개월 이내에 신고해야 하며, 신고하지 않을 경우 가산세가 부과될 수 있으므로 주의해야 합니다.(참고: 상속세법 제18조)]` |
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---
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| Model | 질문 : 재판이 끝난 후에 손해배상 청구를 할 수 있는지 궁금합니다. 부동산 관련 사기를 당해서 자세한 상담을 받고 싶어요 |
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|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Llama-2-ko-7b-Chat | `[예, 재판이 끝난 후에도 민사 소송을 제기할 수 있습니다. 부동산 사기 또는 기타 민사 분쟁과 관련하여 변호사의 도움을 구할 수 있는 몇 가지 옵션이 있습니다:1. 민사 소송: 부동산 사기와 같은 민사 분쟁이 있는 경우 변호사를 고용하여 민사소송을 제기하여 손해 배상을 청구하는 것이 가장 일반적입니다.2. 개인 소장: 민사 사건에서 민사 소장을 작성하여 법원에 제출하는 것도 한 가지 방법입니다.3. 중재: 중재 기관을 통해 분쟁을 해결할 수도 있습니다.4. 형사 고소: 부동산 사기로 형사 고소가 가능한지 확인해야 합니다. 이러한 옵션 중 어떤 것을 선택할지 결정하기 전에 변호사와 상담하여 조언을 구하는 것이 좋습니다.]` |
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| Legal-Llama-2-ko-7b-Chat | `[예, 재판이 끝난 후에도 손해배상 청구가 가능합니다. 민사소송법 제766조(손해배상청구권의 소멸시효)에 따르면, 불법행위로 인한 손해배상의 청구권은 피해자나 그 법정대리인이 그 손해 및 가해자를 안 날로부터 3년간 이를 행사하지 아니하면 시효로 인하여 소멸한다고 규정하고 있습니다.따라서 부동산 사기로 인해 입은 손해를 입증할 수 있는 증거와 증인을 확보하고, 법원에 손해배상을 청구할 수 있습니다. 또한, 부동산 사기를 당한 사실을 경찰에 신고하여 수사를 의뢰하는 것도 좋은 방법입니다.]` |
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---
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### Inference
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```python
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def gen(x, model, tokenizer, device):
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prompt = (
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f"아래는 작업을 설명하는 명령어입니다. 요청을 적절히 완료하는 응답을 작성하세요.\n\n### 명령어:\n{x}\n\n### 응답:"
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)
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len_prompt = len(prompt)
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gened = model.generate(
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**tokenizer(prompt, return_tensors="pt", return_token_type_ids=False).to(
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device
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),
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max_new_tokens=1024,
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early_stopping=True,
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do_sample=True,
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top_k=20,
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top_p=0.92,
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no_repeat_ngram_size=3,
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eos_token_id=2,
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repetition_penalty=1.2,
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num_beams=3
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)
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return tokenizer.decode(gened[0])[len_prompt:]
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def LLM_infer(input):
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device = (
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torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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)
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model_id = "kfkas/Legal-Llama-2-ko-7b-Chat"
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model = AutoModelForCausalLM.from_pretrained(
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model_id, device_map={"": 0},torch_dtype=torch.float16, low_cpu_mem_usage=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model.eval()
|
||||
model.config.use_cache = (True)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
output = gen(input, model=model, tokenizer=tokenizer, device=device)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
text = LLM_infer("음주운전을 하면 어떻게 처벌 받아?")
|
||||
print(text)
|
||||
```
|
||||
|
||||
## Note for oobabooga/text-generation-webui
|
||||
|
||||
Remove `ValueError` at `load_tokenizer` function(line 109 or near), in `modules/models.py`.
|
||||
|
||||
```python
|
||||
diff --git a/modules/models.py b/modules/models.py
|
||||
index 232d5fa..de5b7a0 100644
|
||||
--- a/modules/models.py
|
||||
+++ b/modules/models.py
|
||||
@@ -106,7 +106,7 @@ def load_tokenizer(model_name, model):
|
||||
trust_remote_code=shared.args.trust_remote_code,
|
||||
use_fast=False
|
||||
)
|
||||
- except ValueError:
|
||||
+ except:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
path_to_model,
|
||||
trust_remote_code=shared.args.trust_remote_code,
|
||||
```
|
||||
|
||||
Since Llama-2-Ko uses FastTokenizer provided by HF tokenizers NOT sentencepiece package,
|
||||
it is required to use `use_fast=True` option when initialize tokenizer.
|
||||
|
||||
Apple Sillicon does not support BF16 computing, use CPU instead. (BF16 is supported when using NVIDIA GPU)
|
||||
|
||||
|
||||
---
|
||||
|
||||
> Below is the original model card of the Llama-2 model.
|
||||
|
||||
# **Llama 2**
|
||||
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
|
||||
|
||||
## Model Details
|
||||
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
|
||||
|
||||
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
|
||||
|
||||
**Model Developers** Meta
|
||||
|
||||
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
|
||||
|
||||
**Input** Models input text only.
|
||||
|
||||
**Output** Models generate text only.
|
||||
|
||||
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
|
||||
|
||||
|
||||
||Training Data|Params|Content Length|GQA|Tokens|LR|
|
||||
|---|---|---|---|---|---|---|
|
||||
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|
||||
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|
||||
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
|
||||
|
||||
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
|
||||
|
||||
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
|
||||
|
||||
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
|
||||
|
||||
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
|
||||
|
||||
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
|
||||
|
||||
## Intended Use
|
||||
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
|
||||
|
||||
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
|
||||
|
||||
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
|
||||
|
||||
## Hardware and Software
|
||||
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
|
||||
|
||||
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
|
||||
|
||||
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|
||||
|---|---|---|---|
|
||||
|Llama 2 7B|184320|400|31.22|
|
||||
|Llama 2 13B|368640|400|62.44|
|
||||
|Llama 2 70B|1720320|400|291.42|
|
||||
|Total|3311616||539.00|
|
||||
|
||||
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
|
||||
|
||||
## Training Data
|
||||
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
|
||||
|
||||
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
|
||||
|
||||
## Evaluation Results
|
||||
|
||||
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|
||||
|
||||
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|
||||
|---|---|---|---|---|---|---|---|---|---|
|
||||
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|
||||
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|
||||
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|
||||
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|
||||
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|
||||
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|
||||
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
|
||||
|
||||
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|
||||
|
||||
|||TruthfulQA|Toxigen|
|
||||
|---|---|---|---|
|
||||
|Llama 1|7B|27.42|23.00|
|
||||
|Llama 1|13B|41.74|23.08|
|
||||
|Llama 1|33B|44.19|22.57|
|
||||
|Llama 1|65B|48.71|21.77|
|
||||
|Llama 2|7B|33.29|**21.25**|
|
||||
|Llama 2|13B|41.86|26.10|
|
||||
|Llama 2|70B|**50.18**|24.60|
|
||||
|
||||
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|
||||
|
||||
|
||||
|||TruthfulQA|Toxigen|
|
||||
|---|---|---|---|
|
||||
|Llama-2-Chat|7B|57.04|**0.00**|
|
||||
|Llama-2-Chat|13B|62.18|**0.00**|
|
||||
|Llama-2-Chat|70B|**64.14**|0.01|
|
||||
|
||||
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
|
||||
|
||||
## Ethical Considerations and Limitations
|
||||
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’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. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
|
||||
|
||||
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
|
||||
|
||||
## Reporting Issues
|
||||
Please report any software “bug,” or other problems with the models through one of the following means:
|
||||
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
|
||||
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
|
||||
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
|
||||
|
||||
## Llama Model Index
|
||||
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|
||||
|---|---|---|---|---|
|
||||
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|
||||
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|
||||
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
||||
27
config.json
Normal file
27
config.json
Normal file
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"_name_or_path": "beomi/llama-2-ko-7b",
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_length": 4096,
|
||||
"max_position_embeddings": 2048,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"pad_token_id": 0,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.31.0.dev0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 46336
|
||||
}
|
||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"max_length": 4096,
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.31.0.dev0"
|
||||
}
|
||||
3
pytorch_model-00001-of-00002.bin
Normal file
3
pytorch_model-00001-of-00002.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8c725316c243b4c97a560c1ceb0ec2d5948351d4909c192cacac14b2d0ad9802
|
||||
size 9913697075
|
||||
3
pytorch_model-00002-of-00002.bin
Normal file
3
pytorch_model-00002-of-00002.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ffd96605bbfda82f79b615935127d0366d739531bb2076ada787e2f64acf271c
|
||||
size 3798126307
|
||||
330
pytorch_model.bin.index.json
Normal file
330
pytorch_model.bin.index.json
Normal file
@@ -0,0 +1,330 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 13711720448
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "pytorch_model-00002-of-00002.bin",
|
||||
"model.embed_tokens.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.0.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.1.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.10.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.11.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.11.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.11.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.11.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
254239
tokenizer.json
Normal file
254239
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
32
tokenizer_config.json
Normal file
32
tokenizer_config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<s>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user