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language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
quantized_by: iarbel
model_name: Llama-2-70B-chat - bnb 4-bit
Llama-2-70B-chat - bitsandbytes 4-bit
This repository contains the bitsandbytes 4-bit quantized version of meta-llama/Llama-2-70b-chat-hf. To use it, make sure to have the latest version of bitsandbytes and transformers installed from source:
Loading this model as such: will directly load the quantized model in 4-bit precision.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit"
model = AutoModelForCausalLM.from_pretrained(model_id)
NOTICE: This model follows the same license and terms as all Llama-2 models. Refer to meta-llama repos for additional details.
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 70B fine-tuned model, optimized for dialogue use cases and 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 — 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 10-4 |
| 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/
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.
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(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.