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llama-2
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llama-2-ko
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language:
  - en
  - ko
pipeline_tag: text-generation
inference: false
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - llama-2
  - kollama
  - llama-2-ko

Llama-2-Ko ๐Ÿฆ™๐Ÿ‡ฐ๐Ÿ‡ท

Llama-2-Ko serves as an advanced iteration of Llama 2, benefiting from an expanded vocabulary and the inclusion of a Korean corpus in its further pretraining. Just like its predecessor, Llama-2-Ko operates within the broad range of generative text models that stretch from 7 billion to 70 billion parameters. This repository focuses on the 7B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below.

Model Details

Model Developers Junbum Lee (Beomi)

Variations Llama-2-Ko will come in a range of parameter sizes โ€” 7B, 13B, and 70B โ€” as well as pretrained and fine-tuned variations.

Input Models input text only.

Output Models generate text only.

Model Architecture

Llama-2-Ko is an auto-regressive language model that uses an optimized transformer architecture based on Llama-2.

Training Data Params Content Length GQA Tokens LR
Llama 2 A new mix of Korean online data 7B 4k โœ— >20B 1e-5

Vocab Expansion

  • Original Llama-2: 32000 Sentencepiece BPE
  • Expanded Llama-2-ko: 46336 Sentencepiece BPE
    • New vocab and merges, trained with Korean Corpus
  • Tokenizer Examples: Llama-2 vs Llama-2-Ko
    • Use the same tokenization for English, but a shorter/merged tokenization for Korean.
    • Tokenize "์•ˆ๋…•ํ•˜์„ธ์š”, ์˜ค๋Š˜์€ ๋‚ ์”จ๊ฐ€ ์ฐธ ์ข‹๋„ค์š”."
      • Llama-2:
        ['โ–', '์•ˆ', '<0xEB>', '<0x85>', '<0x95>', 'ํ•˜', '์„ธ', '์š”', ',', 'โ–', '์˜ค', '<0xEB>', '<0x8A>', '<0x98>', '์€', 'โ–', '<0xEB>', '<0x82>', '<0xA0>', '์”จ', '๊ฐ€', 'โ–', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '์š”']
      • Llama-2-Ko:
        ['โ–์•ˆ๋…•', 'ํ•˜์„ธ์š”', ',', 'โ–์˜ค๋Š˜์€', 'โ–๋‚ ', '์”จ๊ฐ€', 'โ–์ข‹๋„ค์š”']
    • Tokenize "Llama 2: Open Foundation and Fine-Tuned Chat Models"
      • Llama-2:
        ['โ–L', 'l', 'ama', 'โ–', '2', ':', 'โ–Open', 'โ–Foundation', 'โ–and', 'โ–Fine', '-', 'T', 'un', 'ed', 'โ–Ch', 'at', 'โ–Mod', 'els']
      • Llama-2-Ko:
        ['โ–L', 'l', 'ama', 'โ–', '2', ':', 'โ–Open', 'โ–Foundation', 'โ–and', 'โ–Fine', '-', 'T', 'un', 'ed', 'โ–Ch', 'at', 'โ–Mod', 'els']

Model Benchmark

LM Eval Harness - Korean (polyglot branch)

NSMC (Acc) - 50000 full test

TBD

COPA (F1)

Model 0-shot 5-shot 10-shot 50-shot
https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 0.6696 0.6477 0.6419 0.6514
https://huggingface.co/kakaobrain/kogpt 0.7345 0.7287 0.7277 0.7479
https://huggingface.co/facebook/xglm-7.5B 0.6723 0.6731 0.6769 0.7119
https://huggingface.co/EleutherAI/polyglot-ko-1.3b 0.7196 0.7193 0.7204 0.7206
https://huggingface.co/EleutherAI/polyglot-ko-3.8b 0.7595 0.7608 0.7638 0.7788
https://huggingface.co/EleutherAI/polyglot-ko-5.8b 0.7745 0.7676 0.7775 0.7887
https://huggingface.co/EleutherAI/polyglot-ko-12.8b 0.7937 0.8108 0.8037 0.8369
Llama-2 Original 7B* 0.562033 0.575982 0.576216 0.595532
Llama-2-Ko-7b 20B (10k) 0.738780 0.762639 0.780761 0.797863

*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (No tokenizer updated)

HellaSwag (F1)

Model 0-shot 5-shot 10-shot 50-shot
https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 0.5243 0.5272 0.5166 0.5352
https://huggingface.co/kakaobrain/kogpt 0.5590 0.5833 0.5828 0.5907
https://huggingface.co/facebook/xglm-7.5B 0.5665 0.5689 0.5565 0.5622
https://huggingface.co/EleutherAI/polyglot-ko-1.3b 0.5247 0.5260 0.5278 0.5427
https://huggingface.co/EleutherAI/polyglot-ko-3.8b 0.5707 0.5830 0.5670 0.5787
https://huggingface.co/EleutherAI/polyglot-ko-5.8b 0.5976 0.5998 0.5979 0.6208
https://huggingface.co/EleutherAI/polyglot-ko-12.8b 0.5954 0.6306 0.6098 0.6118
Llama-2 Original 7B* 0.415390 0.431382 0.421342 0.442003
Llama-2-Ko-7b 20B (10k) 0.451757 0.466751 0.472607 0.482776

*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (No tokenizer updated)

BoolQ (F1)

Model 0-shot 5-shot 10-shot 50-shot
https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 0.3356 0.4014 0.3640 0.3560
https://huggingface.co/kakaobrain/kogpt 0.4514 0.5981 0.5499 0.5202
https://huggingface.co/facebook/xglm-7.5B 0.4464 0.3324 0.3324 0.3324
https://huggingface.co/EleutherAI/polyglot-ko-1.3b 0.3552 0.4751 0.4109 0.4038
https://huggingface.co/EleutherAI/polyglot-ko-3.8b 0.4320 0.5263 0.4930 0.4038
https://huggingface.co/EleutherAI/polyglot-ko-5.8b 0.4356 0.5698 0.5187 0.5236
https://huggingface.co/EleutherAI/polyglot-ko-12.8b 0.4818 0.6041 0.6289 0.6448
Llama-2 Original 7B* 0.352050 0.563238 0.474788 0.419222
Llama-2-Ko-7b 20B (10k) 0.360656 0.679743 0.680109 0.662152

*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (No tokenizer updated)

SentiNeg (F1)

Model 0-shot 5-shot 10-shot 50-shot
https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5 0.6065 0.6878 0.7280 0.8413
https://huggingface.co/kakaobrain/kogpt 0.3747 0.8942 0.9294 0.9698
https://huggingface.co/facebook/xglm-7.5B 0.3578 0.4471 0.3964 0.5271
https://huggingface.co/EleutherAI/polyglot-ko-1.3b 0.6790 0.6257 0.5514 0.7851
https://huggingface.co/EleutherAI/polyglot-ko-3.8b 0.4858 0.7950 0.7320 0.7851
https://huggingface.co/EleutherAI/polyglot-ko-5.8b 0.3394 0.8841 0.8808 0.9521
https://huggingface.co/EleutherAI/polyglot-ko-12.8b 0.9117 0.9015 0.9345 0.9723
Llama-2 Original 7B* 0.347502 0.529124 0.480641 0.788457
Llama-2-Ko-7b 20B (10k) 0.485546 0.829503 0.871141 0.851253

*Llama-2 Original 7B used https://huggingface.co/meta-llama/Llama-2-7b-hf (No tokenizer updated)

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 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 โ€” 7B, 13B, and 70B โ€” 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 โœ— 2.0T 3.0 x 10-4
Llama 2 A new mix of publicly available online data 13B 4k โœ— 2.0T 3.0 x 10-4
Llama 2 A new mix of publicly available online data 70B 4k โœ” 2.0T 1.5 x 10-4

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/

Research Paper "Llama-2: Open Foundation and Fine-tuned Chat Models"

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.

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 Metaโ€™s sustainability program.

Time (GPU hours) Power Consumption (W) Carbon Emitted(tCO2eq)
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

CO2 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 2โ€™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/

Reporting Issues

Please report any software โ€œbug,โ€ or other problems with the models through one of the following means:

Llama Model Index

Model Llama2 Llama2-hf Llama2-chat Llama2-chat-hf
7B Link Link Link Link
13B Link Link Link Link
70B Link Link Link Link