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Upstage-Llama-2-70B-instruct-v2-GPTQ

Text Generation
Transformers
Safetensors
English
llama
upstage
llama-2
instruct
instruction
text-generation-inference
4-bit precision
gptq
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Upstage-Llama-2-70B-instruct-v2-GPTQ / README.md
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metadata
inference: false
language:
  - en
license: other
model_creator: Upstage
model_link: https://huggingface.co/upstage/Llama-2-70b-instruct-v2
model_name: Llama 2 70B Instruct v2
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
tags:
  - upstage
  - llama-2
  - instruct
  - instruction
TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Llama 2 70B Instruct v2 - GPTQ

Description

This repo contains GPTQ model files for Upstage's Llama 2 70B Instruct v2.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

Repositories available

Prompt template: Orca-Hashes 

### System:
This is a system prompt, please behave and help the user.

### User:
{prompt}

### Assistant:

Provided files

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

Branch Bits Group Size Act Order (desc_act) GPTQ Dataset Size ExLlama Compat? Made With Desc
main 4 None Yes wikitext 35.33 GB Yes AutoGPTQ Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options.
gptq-4bit-32g-actorder_True 4 32 Yes wikitext 40.66 GB Yes AutoGPTQ 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed.
gptq-4bit-64g-actorder_True 4 64 Yes wikitext 37.99 GB Yes AutoGPTQ 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-4bit-128g-actorder_True 4 128 Yes wikitext 36.65 GB Yes AutoGPTQ 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-3bit--1g-actorder_True 3 None Yes wikitext 26.78 GB No AutoGPTQ 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g.
gptq-3bit-128g-actorder_False 3 128 No wikitext 28.03 GB No AutoGPTQ 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None.
gptq-3bit-128g-actorder_True 3 128 Yes wikitext 28.03 GB No AutoGPTQ 3-bit, with group size 128g and act-order. Higher quality than 128g-False but poor AutoGPTQ CUDA speed.
gptq-3bit-64g-actorder_True 3 64 Yes wikitext 29.30 GB No AutoGPTQ 3-bit, with group size 64g and act-order. Highest quality 3-bit option. Poor AutoGPTQ CUDA speed.

How to download from branches

  • In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ:gptq-4bit-32g-actorder_True
  • With Git, you can clone a branch with:
git clone --branch --single-branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ
  • In Python Transformers code, the branch is the revision parameter; see below.

How to easily download and use this model in text-generation-webui.

Please make sure you're using the latest version of text-generation-webui.

It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.

  1. Click the Model tab.
  2. Under Download custom model or LoRA, enter TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ.
  • To download from a specific branch, enter for example TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ:gptq-4bit-32g-actorder_True
  • see Provided Files above for the list of branches for each option.
  1. Click Download.
  2. The model will start downloading. Once it's finished it will say "Done"
  3. In the top left, click the refresh icon next to Model.
  4. In the Model dropdown, choose the model you just downloaded: Upstage-Llama-2-70B-instruct-v2-GPTQ
  5. The model will automatically load, and is now ready for use!
  6. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
  • Note that you do not need to set GPTQ parameters any more. These are set automatically from the file quantize_config.json.
  1. Once you're ready, click the Text Generation tab and enter a prompt to get started!

How to use this GPTQ model from Python code

First make sure you have AutoGPTQ installed:

GITHUB_ACTIONS=true pip install auto-gptq

Then try the following example code:

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

model_name_or_path = "TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ"
model_basename = "model"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

"""
To download from a specific branch, use the revision parameter, as in this example:

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        revision="gptq-4bit-32g-actorder_True",
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        quantize_config=None)
"""

prompt = "Tell me about AI"
prompt_template=f'''### System:
This is a system prompt, please behave and help the user.

### User:
{prompt}

### Assistant:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Aemon Algiz.

Patreon special mentions: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Upstage's Llama 2 70B Instruct v2

LLaMa-2-70b-instruct-v2 model card

Model Details

Dataset Details

Used Datasets

  • Orca-style dataset
  • Alpaca-Style Dataset

Prompt Template 

### System:
{System}
### User:
{User}
### Assistant:
{Assistant}

Usage

Tested on A100 80GB

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
model = AutoModelForCausalLM.from_pretrained(
    "upstage/Llama-2-70b-instruct-v2",
    device_map='auto',
    torch_dtype=torch.float16,
    load_in_8bit=True,
    rope_scaling={'type': 'dynamic', 'factor': 2} # longer inputs possible
)
prompt = "### User:\nThomas is very healthy, but he has to go to the hospital every day. What could be the reasons?\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
del inputs['token_type_ids']
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

Our model can handle >10k input tokens thanks to the rope_scaling option.

Hardware and Software

Evaluation Results

Overview

Main Results

Model H4 Average ARC HellaSwag MMLU TruthfulQA MT_Bench
Llama-2-70b-instruct-v2 (Ours, Local Reproduction) 72.7 71.6 87.7 69.7 61.6 7.440625
Llama-2-70b-instruct (Ours, Local Reproduction) 72.0 70.7 87.4 69.3 60.7 7.24375
llama-65b-instruct (Ours, Local Reproduction) 69.4 67.6 86.5 64.9 58.8
Llama-2-70b-hf 67.3 67.3 87.3 69.8 44.9
llama-30b-instruct-2048 (Ours, Open LLM Leaderboard) 67.0 64.9 84.9 61.9 56.3
llama-30b-instruct-2048 (Ours, Local Reproduction) 67.0 64.9 85.0 61.9 56.0 6.88125
llama-30b-instruct (Ours, Open LLM Leaderboard) 65.2 62.5 86.2 59.4 52.8
llama-65b 64.2 63.5 86.1 63.9 43.4
falcon-40b-instruct 63.4 61.6 84.3 55.4 52.5

Scripts

  • Prepare evaluation environments:
# clone the repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# check out the specific commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# change to the repository directory
cd lm-evaluation-harness

Ethical Issues

Ethical Considerations

  • There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process.

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