Hugging Face's logo

zohaib99k
/
Nous-Hermes-Llama2-8bit-GPTQ

Text Generation
Transformers
English
llama
llama-2
self-instruct
distillation
synthetic instruction
Model card Files Community
Nous-Hermes-Llama2-8bit-GPTQ / README.md
zohaib99k's picture
zohaib99k
Upload 13 files
7483809
preview code
|
raw
history blame contribute delete
18.2 kB
metadata
inference: false
language:
  - en
license: other
model_type: llama
tags:
  - llama-2
  - self-instruct
  - distillation
  - synthetic instruction
TheBlokeAI

Nous Research's Nous Hermes Llama 2 13B GPTQ

These files are GPTQ model files for Nous Research's Nous Hermes Llama 2 13B.

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: Alpaca 

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:

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) File Size ExLlama Compatible? Made With Description
main 4 128 False 7.26 GB True 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 True 8.00 GB True 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 True 7.51 GB True 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 True 7.26 GB True AutoGPTQ 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-8bit-128g-actorder_True 8 128 True 13.65 GB False AutoGPTQ 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed.
gptq-8bit-64g-actorder_True 8 64 True 13.95 GB False AutoGPTQ 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed.
gptq-8bit-128g-actorder_False 8 128 False 13.65 GB False AutoGPTQ 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed.
gptq-8bit--1g-actorder_True 8 None True 13.36 GB False AutoGPTQ 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed.

How to download from branches

  • In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Nous-Hermes-Llama2-GPTQ:gptq-4bit-32g-actorder_True
  • With Git, you can clone a branch with:
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Nous-Hermes-Llama2-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/Nous-Hermes-Llama2-GPTQ.
  • To download from a specific branch, enter for example TheBloke/Nous-Hermes-Llama2-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: Nous-Hermes-Llama2-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/Nous-Hermes-Llama2-GPTQ"
model_basename = "gptq_model-4bit-128g"

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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:
'''

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: Luke from CarbonQuill, Aemon Algiz.

Patreon special mentions: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse

Thank you to all my generous patrons and donaters!

Original model card: Nous Research's Nous Hermes Llama 2 13B

Model Card: Nous-Hermes-Llama2-13b

Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.

Model Description

Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.

This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.

This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.

Example Outputs:

Example4 Example1 Example2 Example3

Model Training

The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.

This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below

Collaborators

The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI.

Special mention goes to @winglian for assisting in some of the training issues.

Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.

Among the contributors of datasets:

  • GPTeacher was made available by Teknium
  • Wizard LM by nlpxucan
  • Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
  • GPT4-LLM and Unnatural Instructions were provided by Microsoft
  • Airoboros dataset by jondurbin
  • Camel-AI's domain expert datasets are from Camel-AI
  • CodeAlpaca dataset by Sahil 2801.

If anyone was left out, please open a thread in the community tab.

Prompt Format

The model follows the Alpaca prompt format:

### Instruction:
<prompt>

### Response:
<leave a newline blank for model to respond>

or 

### Instruction:
<prompt>

### Input:
<additional context>

### Response:
<leave a newline blank for model to respond>

Benchmark Results

AGI-Eval

|             Task             |Version| Metric |Value |   |Stderr|
|agieval_aqua_rat              |      0|acc     |0.2362|±  |0.0267|
|                              |       |acc_norm|0.2480|±  |0.0272|
|agieval_logiqa_en             |      0|acc     |0.3425|±  |0.0186|
|                              |       |acc_norm|0.3472|±  |0.0187|
|agieval_lsat_ar               |      0|acc     |0.2522|±  |0.0287|
|                              |       |acc_norm|0.2087|±  |0.0269|
|agieval_lsat_lr               |      0|acc     |0.3510|±  |0.0212|
|                              |       |acc_norm|0.3627|±  |0.0213|
|agieval_lsat_rc               |      0|acc     |0.4647|±  |0.0305|
|                              |       |acc_norm|0.4424|±  |0.0303|
|agieval_sat_en                |      0|acc     |0.6602|±  |0.0331|
|                              |       |acc_norm|0.6165|±  |0.0340|
|agieval_sat_en_without_passage|      0|acc     |0.4320|±  |0.0346|
|                              |       |acc_norm|0.4272|±  |0.0345|
|agieval_sat_math              |      0|acc     |0.2909|±  |0.0307|
|                              |       |acc_norm|0.2727|±  |0.0301|

GPT-4All Benchmark Set

|    Task     |Version| Metric |Value |   |Stderr|
|arc_challenge|      0|acc     |0.5102|±  |0.0146|
|             |       |acc_norm|0.5213|±  |0.0146|
|arc_easy     |      0|acc     |0.7959|±  |0.0083|
|             |       |acc_norm|0.7567|±  |0.0088|
|boolq        |      1|acc     |0.8394|±  |0.0064|
|hellaswag    |      0|acc     |0.6164|±  |0.0049|
|             |       |acc_norm|0.8009|±  |0.0040|
|openbookqa   |      0|acc     |0.3580|±  |0.0215|
|             |       |acc_norm|0.4620|±  |0.0223|
|piqa         |      0|acc     |0.7992|±  |0.0093|
|             |       |acc_norm|0.8069|±  |0.0092|
|winogrande   |      0|acc     |0.7127|±  |0.0127|

BigBench Reasoning Test

|                      Task                      |Version|       Metric        |Value |   |Stderr|

|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5526|±  |0.0362|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7344|±  |0.0230|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.2636|±  |0.0275|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.0195|±  |0.0073|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2760|±  |0.0200|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2100|±  |0.0154|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4400|±  |0.0287|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2440|±  |0.0192|
|bigbench_navigate                               |      0|multiple_choice_grade|0.4950|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5570|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.3728|±  |0.0229|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1854|±  |0.0123|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.6298|±  |0.0360|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.6156|±  |0.0155|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3140|±  |0.0147|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2032|±  |0.0114|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1406|±  |0.0083|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4400|±  |0.0287|

These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:

  • GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1
  • 0.3657 on BigBench, up from 0.328 on hermes-llama1
  • 0.372 on AGIEval, up from 0.354 on Hermes-llama1

These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position.

Resources for Applied Use Cases:

For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord
For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot

Future Plans

We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.

Model Usage

The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.