- 模型資訊
- 模型資料
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.
來自Meta開發(fā)并公開發(fā)布的，LLaMa 2系列的大型語言模型（LLMs），其規(guī)模從70億到700億參數不等。該系列模型提供了多種參數大小——7B、13B和70B等——以及預訓練和微調的變體。本模型為7B規(guī)模的預訓練版本，并適配到ModelScope生態(tài)，可以通過ModelScope library加載。
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 — 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.
注意：使用此模型受Meta許可證的約束。為了下載模型權重和分詞器，請訪問網站并在此處請求訪問前接受我們的許可證。
Meta開發(fā)并公開發(fā)布了Llama 2系列的大型語言模型（LLMs），這是一系列預訓練和微調的生成文本模型，規(guī)模從70億到700億參數不等。我們微調的LLMs，稱為Llama-2-Chat，專為對話用例進行優(yōu)化。在我們測試的大多數基準測試中，Llama-2-Chat模型的表現(xiàn)優(yōu)于開源聊天模型，并且在我們對幫助性和安全性的人類評估中，與一些流行的閉源模型如ChatGPT和PaLM相當。
模型開發(fā)者 Meta
變體 Llama 2有多種參數大小——7B、13B和70B——以及預訓練和微調的變體。
輸入 模型只接受文本輸入。
輸出 模型只生成文本。
模型架構 Llama 2是一種自回歸語言模型，使用優(yōu)化的變壓器架構。調整版本使用監(jiān)督微調（SFT）和人類反饋的強化學習（RLHF）來符合人類對幫助性和安全性的偏好。
| 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 10-4 |
| Llama 2 | A new mix of publicly available online data | 13B | 4k | ? | 2.0T | 3.0 x 10-4 |
| Llama 2 | A new mix of publicly available online data | 70B | 4k | ? | 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/
示例代碼
推理代碼
import torch
from modelscope import Model, AutoTokenizer
model = Model.from_pretrained("modelscope/Llama-2-7b-ms", revision='v1.0.1', device_map='auto', torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("modelscope/Llama-2-7b-ms", revision='v1.0.1')
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids.to(model.device), max_length=30)
print(tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])
SFT
代碼鏈接: https://github.com/modelscope/swift/tree/main/examples/pytorch/llm
- 支持的sft方法: lora, qlora, 全參數微調, …
- 支持的模型: llama2-7b, llama2-13b, llama2-70b, …
- 支持的特性: 模型量化, DDP, 模型并行(device_map), gradient checkpoint, 梯度累加, 支持推送modelscope hub, 支持自定義數據集, 兼容notebook, …
使用qlora sft llama2-7b的腳本 (需要8G顯存)
git clone https://github.com/modelscope/swift.git
cd swift/examples/pytorch/llm
CUDA_VISIBLE_DEVICES=0 \
python llm_sft.py \
--model_type llama2-7b \
--sft_type lora \
--output_dir runs \
--dataset alpaca-en,alpaca-zh \
--dataset_sample 20000 \
--max_length 1024 \
--quantization_bit 4 \
--lora_rank 8 \
--lora_alpha 32 \
--lora_dropout_p 0.1 \
--batch_size 1 \
--learning_rate 1e-4 \
--gradient_accumulation_steps 16 \
--eval_steps 50 \
--save_steps 50 \
--save_total_limit 2 \
--logging_steps 10 \
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.
預期用例 Llama 2預期用于商業(yè)和研究用途，語言為英語。調整模型預期用于類似助手的聊天，而預訓練模型可以適應各種自然語言生成任務。
為了獲得聊天版本的預期特性和性能，需要遵循特定的格式，包括INST和<
超出范圍的用途 以任何違反適用法律或法規(guī)（包括貿易合規(guī)法）的方式使用。使用英語以外的語言。以任何其他方式使用，這被Llama 2的可接受使用政策和許可協(xié)議所禁止。
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.
訓練因素 我們使用了定制的訓練庫，Meta的研究超級集群，以及用于預訓練的生產集群。微調、注釋和評估也在第三方云計算上進行。
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.
碳足跡 預訓練使用了累計330萬GPU小時的計算，硬件類型為A100-80GB（TDP為350-400W）。估計的總排放量為539 tCO2eq，其中100%由Meta的可持續(xù)性計劃抵消。
| 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.
預訓練期間的CO2排放。時間：訓練每個模型所需的總GPU時間。功耗：根據功耗效率調整的每個GPU設備的峰值功率容量。100%的排放直接由Meta的可持續(xù)性計劃抵消，因為我們公開發(fā)布這些模型，所以不需要其他人承擔預訓練的成本。
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.
概述 Llama 2在來自公開可用源的2萬億令牌的數據上進行了預訓練。微調數據包括公開可用的指令數據集，以及超過一百萬個新的人類注釋示例。預訓練和微調數據集都不包括Meta用戶數據。
Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
數據新鮮度 預訓練數據的截止日期為2022年9月，但一些調整數據更近，最近的數據為2023年7月。
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/
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
- Reporting problematic content generated by the model: developers.facebook.com/llama_output_feedback
- Reporting bugs and security concerns: facebook.com/whitehat/info