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

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TheBlokeAI

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

Llama2 13B Orca 8K 3319 - GGML

Description

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

Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, llama.cpp no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.

About GGML

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

  • text-generation-webui, the most popular web UI. Supports NVidia CUDA GPU acceleration.
  • KoboldCpp, a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
  • LM Studio, a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
  • LoLLMS Web UI, a great web UI with CUDA GPU acceleration via the c_transformers backend.
  • 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.

Repositories available

Prompt template: OpenAssistant-System 

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

Compatibility

These quantised GGML files are compatible with llama.cpp between June 6th (commit 2d43387) and August 21st 2023.

For support with latest llama.cpp, please use GGUF files instead.

The final llama.cpp commit with support for GGML was: dadbed99e65252d79f81101a392d0d6497b86caa

As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.

Explanation of the new k-quant 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
  • GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

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.ggmlv3.q2_K.bin q2_K 2 5.74 GB 8.24 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q3_K_S.bin q3_K_S 3 5.87 GB 8.37 GB New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q3_K_M.bin q3_K_M 3 6.53 GB 9.03 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q3_K_L.bin q3_K_L 3 7.14 GB 9.64 GB New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q4_0.bin q4_0 4 7.32 GB 9.82 GB Original quant method, 4-bit.
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q4_K_S.bin q4_K_S 4 7.56 GB 10.06 GB New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q4_K_M.bin q4_K_M 4 8.06 GB 10.56 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q4_1.bin q4_1 4 8.14 GB 10.64 GB Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q5_0.bin q5_0 5 8.95 GB 11.45 GB Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q5_K_S.bin q5_K_S 5 9.15 GB 11.65 GB New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q5_K_M.bin q5_K_M 5 9.40 GB 11.90 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q5_1.bin q5_1 5 9.76 GB 12.26 GB Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q6_K.bin q6_K 6 10.83 GB 13.33 GB New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
openassistant-llama2-13b-orca-8k-3319.ggmlv3.q8_0.bin q8_0 8 13.83 GB 16.33 GB Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

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.

How to run in llama.cpp

Make sure you are using llama.cpp from commit dadbed99e65252d79f81101a392d0d6497b86caa or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

./main -t 10 -ngl 32 -m openassistant-llama2-13b-orca-8k-3319.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|system|>You are a story writing assistant.</s><|prompter|>Write a story about llamas</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.

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

Change -c 2048 to the desired sequence length for this model. For example, -c 4096 for a Llama 2 model. For models that use RoPE, add --rope-freq-base 10000 --rope-freq-scale 0.5 for doubled context, or --rope-freq-base 10000 --rope-freq-scale 0.25 for 4x context.

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.

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.
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Datasets used to train TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGML