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OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF

Text Generation
Transformers
GGUF
4 datasets
English
llama
sft
text-generation-inference
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OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF / README.md
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metadata
datasets:
  - ehartford/dolphin
  - shahules786/orca-chat
  - togethercomputer/RedPajama-Data-1T
  - atom-in-the-universe/fanfics-10k-50k
inference: false
language:
  - en
license: llama2
model_creator: OpenAssistant
model_link: https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319
model_name: Llama2 13B Orca 8K 3319
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
tags:
  - sft
widget:
  - text: >-
      <|system|>You are an AI assistant. You will be given a task. You must
      generate a detailed and long answer.</s><|prompter|>What is a meme, and
      what's the history behind this word?</s><|assistant|>
  - text: >-
      <|system|>You are an AI assistant that helps people find
      information.</s><|prompter|>What's the Earth total
      population</s><|assistant|>
  - text: >-
      <|system|>You are an AI assistant that follows instruction extremely well.
      Help as much as you can.</s><|prompter|>Write a story about future of AI
      development</s><|assistant|>
TheBlokeAI

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

Llama2 13B Orca 8K 3319 - GGUF

Description

This repo contains GGUF format model files for OpenAssistant's Llama2 13B Orca 8K 3319.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.

Here are a list of clients and libraries that are known to support GGUF:

  • llama.cpp.
  • text-generation-webui, the most widely used web UI, with many features and powerful extensions.
  • KoboldCpp, a fully featured web UI, with full GPU accel across multiple platforms and GPU architectures. Especially good for story telling.
  • LM Studio, an easy-to-use and powerful local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
  • LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
  • ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
  • llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
  • candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

Repositories available

Prompt template: OpenAssistant-System 

<|system|>{system_message}</s><|prompter|>{prompt}</s><|assistant|>

Compatibility

These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit 6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9

They are now also compatible with many third party UIs and libraries - please see the list at the top of the README.

Explanation of quantisation methods

Click to see details

The new methods available are:

  • GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
  • GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
  • GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
  • GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
  • GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name Quant method Bits Size Max RAM required Use case
openassistant-llama2-13b-orca-8k-3319.Q2_K.gguf Q2_K 2 5.43 GB 7.93 GB smallest, significant quality loss - not recommended for most purposes
openassistant-llama2-13b-orca-8k-3319.Q3_K_S.gguf Q3_K_S 3 5.66 GB 8.16 GB very small, high quality loss
openassistant-llama2-13b-orca-8k-3319.Q3_K_M.gguf Q3_K_M 3 6.34 GB 8.84 GB very small, high quality loss
openassistant-llama2-13b-orca-8k-3319.Q3_K_L.gguf Q3_K_L 3 6.93 GB 9.43 GB small, substantial quality loss
openassistant-llama2-13b-orca-8k-3319.Q4_0.gguf Q4_0 4 7.37 GB 9.87 GB legacy; small, very high quality loss - prefer using Q3_K_M
openassistant-llama2-13b-orca-8k-3319.Q4_K_S.gguf Q4_K_S 4 7.41 GB 9.91 GB small, greater quality loss
openassistant-llama2-13b-orca-8k-3319.Q4_K_M.gguf Q4_K_M 4 7.87 GB 10.37 GB medium, balanced quality - recommended
openassistant-llama2-13b-orca-8k-3319.Q5_0.gguf Q5_0 5 8.97 GB 11.47 GB legacy; medium, balanced quality - prefer using Q4_K_M
openassistant-llama2-13b-orca-8k-3319.Q5_K_S.gguf Q5_K_S 5 8.97 GB 11.47 GB large, low quality loss - recommended
openassistant-llama2-13b-orca-8k-3319.Q5_K_M.gguf Q5_K_M 5 9.23 GB 11.73 GB large, very low quality loss - recommended
openassistant-llama2-13b-orca-8k-3319.Q6_K.gguf Q6_K 6 10.68 GB 13.18 GB very large, extremely low quality loss
openassistant-llama2-13b-orca-8k-3319.Q8_0.gguf Q8_0 8 13.83 GB 16.33 GB very large, extremely low quality loss - not recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

Example llama.cpp command

Make sure you are using llama.cpp from commit 6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9 or later.

For compatibility with older versions of llama.cpp, or for any third-party libraries or clients that haven't yet updated for GGUF, please use GGML files instead.

./main -t 10 -ngl 32 -m openassistant-llama2-13b-orca-8k-3319.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|system|>{system_message}</s><|prompter|>{prompt}</s><|assistant|>"

Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8. If offloading all layers to GPU, set -t 1.

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 4096 to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text-generation-webui

Further instructions here: text-generation-webui/docs/llama.cpp.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model from Python using ctransformers

First install the package 

# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers

Simple example code to load one of these GGUF models 

from ctransformers import AutoModelForCausalLM

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF", model_file="openassistant-llama2-13b-orca-8k-3319.q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))

How to use with LangChain

Here's guides on using llama-cpp-python or ctransformers with LangChain:

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: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: OpenAssistant's Llama2 13B Orca 8K 3319

llama2-13b-orca-8k-3319

Model Description

This model is a fine-tuning of Meta's Llama2 13B model with 8K context size on a long-conversation variant of the Dolphin dataset (orca-chat).

Note: At least Huggingface Transformers 4.31.0 is required to load this model!

Usage 

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/llama2-13b-orca-8k-3319", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/llama2-13b-orca-8k-3319", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")

system_message = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
user_prompt = "Write me a poem please"
prompt = f"""<|system|>{system_message}</s><|prompter|>{user_prompt}</s><|assistant|>"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Model Details

Long context (RoPE Scaling)

This model was fine-tuned with a context size of 8192 tokens using linear scaling of RoPE embeddings. This feature was recently added to Huggingface transformers. Before loading this model please make sure HF transformers >=4.31.0 is installed (pip install transformers>=4.31.0).

Conversation Template

For the initial response use (e.g. the llama2 default system prompt works well):

<|system|>system message</s><|prompter|>user prompt</s><|assistant|>

For multi-turn conversations use:

<|system|>system message</s><|prompter|>Q1</s><|assistant|>A1</s><|prompter|>Q2</s><|assistant|>

The model was trained with the following 15 system messages used to generate the training examples (see ORCA paper):

  1. You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.
  2. You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
  3. You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old.
  4. You are an AI assistant that follows instruction extremely well. Help as much as you can.
  5. You are an AI assistant that helps people find information. Provide a detailed answer so user don’t need to search outside to understand the answer.
  6. You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
  7. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. Think like you are answering to a five year old.
  8. Explain how you used the definition to come up with the answer.
  9. You are an AI assistant. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. You might need to use additional knowledge to answer the question.
  10. You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-by- step and justify your answer.
  11. User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.
  12. You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides and how to use those guidelines to find the answer.
  13. You are an AI assistant, who knows every language and how to translate one language to another. Given a task, you explain in simple steps what the task is asking, any guidelines that it provides. You solve the task and show how you used the guidelines to solve the task.
  14. Given a definition of a task and a sample input, break the definition into small parts. Each of those parts will have some instruction. Explain their meaning by showing an example that meets the criteria in the instruction. Use the following format: Part #: a key part of the definition. Usage: Sample response that meets the criteria from the key part. Explain why you think it meets the criteria.
  15. You are an AI assistant that helps people find information.

Datasets: Orca-Chat/Dolphin, RedPajama1T & FanFics

This model was trained on:

Dataset Composition:
    Tain (sampled):
       orca-chat: 188842 (100%)
       fanfics: 47760 (100%)
       red_pajama: 188262 (25%)
    Valid:
       orca-chat: 5000
       fanfics: 1000
       red_pajama: 1000

The dataset shahules786/orca-chat combines similar examples of the GPT-4 subset of ehartford/dolphin to form longer conversations to improve long-context training.

Additionally, RedPajama and FanFics were used for classic language modelling as an auxiliary task to improve the RoPE scaling for the 8k context size.

Model Configuration 

llama2_13b_orca_8k:
  rng_seed: 0xe1291f1a
  use_custom_sampler: true
  sort_by_length: false
  dtype: fp16
  log_dir: "llama2_log_13b_orca_8k"
  learning_rate: 1e-5
  model_name: /mnt/data/llama2/Llama-2-13b-hf/
  output_dir: llama2_13b_orca_8k
  deepspeed_config: configs/zero_config_pretrain.json
  weight_decay: 0.0
  max_length: 8192
  warmup_steps: 100
  use_flash_attention: true
  gradient_checkpointing: true
  gradient_accumulation_steps: 8
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 1
  residual_dropout: 0.0
  eval_steps: 200
  save_steps: 1000  # (total steps: 3319)
  num_train_epochs: 1
  save_total_limit: 4
  superhot: true
  superhot_config:
    type: linear
    scale: 2
  datasets:
    - orca-chat:
        max_val_set: 5000
    - fanfics:
        max_chunk_size: 65535
        max_val_set: 1000
    - red_pajama:
        fraction: 0.25
        max_val_set: 1000
        max_chunk_size: 65535
  peft_model: false

Developers

Special Thanks

We want to especially thank Eric Hartford who spared no expense in replicating ORCA and making it available at ehartford/dolphin! Also, shoutout to the whole team working on LLongMA-2-13b & the scaled-rope repository for their awesome work: bloc97, jquesnelle & conceptofmind!

The whole Open-Assistant team is very grateful for the continued support of Redmond.ai who sponsored the training compute required for this model.

License

  • Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
  • Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials.