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OpenOrcaxOpenChat-Preview2-13B-GGML

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OpenOrca x OpenChat - Preview2 - 13B - GGML

Description

This repo contains GGML format model files for Open-Orca's OpenOrca x OpenChat - Preview2 - 13B.

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: OpenChat Llama2 V1 

User: {prompt}<|end_of_turn|>Assistant:

Compatibility

Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0

These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.

New k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K

These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.

They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.

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
openorcaxopenchat-preview2-13b.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.
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.ggmlv3.q4_0.bin q4_0 4 7.32 GB 9.82 GB Original quant method, 4-bit.
openorcaxopenchat-preview2-13b.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.
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.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.
openorcaxopenchat-preview2-13b.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.
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.ggmlv3.q5_K_S.bin q5_K_S 5 9.14 GB 11.64 GB New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
openorcaxopenchat-preview2-13b.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
openorcaxopenchat-preview2-13b.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

I use the following command line; adjust for your tastes and needs:

./main -t 10 -ngl 32 -m openorcaxopenchat-preview2-13b.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"

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.

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

How to run in text-generation-webui

Further instructions here: text-generation-webui/docs/llama.cpp-models.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: Luke from CarbonQuill, Aemon Algiz.

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

Thank you to all my generous patrons and donaters!

Original model card: Open-Orca's OpenOrca x OpenChat - Preview2 - 13B

🐋 The Second OpenOrca Model Preview! 🐋

OpenOrca Logo

OpenOrca x OpenChat - Preview2 - 13B

We have used our own OpenOrca dataset to fine-tune Llama2-13B using OpenChat packing. This dataset is our attempt to reproduce the dataset generated for Microsoft Research's Orca Paper.

This second preview release is trained on a curated filtered subset of most of our GPT-4 augmented data.

This release highlights that our dataset and training methods have surpassed performance parity with the Orca paper. We measured this with BigBench-Hard and AGIEval results with the same methods as used in the Orca paper, finding ~103% of original Orca's performance on average. As well, this is done with <1/10th the compute requirement and using <20% of the dataset size from the original Orca paper.

We have run extensive evaluations internally and expect this model to place number 1 on both the HuggingFaceH4 Open LLM Leaderboard and the GPT4ALL Leaderboard for 13B models.

"One" of OpenChat has joined our team, and we'd like to provide special thanks for their training of this model! We have utilized OpenChat MultiPack algorithm which achieves 99.85% bin-packing efficiency on our dataset. This has significantly reduced training time, with efficiency improvement of 3-10X over traditional methods.

Want to visualize our full (pre-filtering) dataset? Check out our Nomic Atlas Map.

Atlas Nomic Dataset Map

We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners.

We will also give sneak-peak announcements on our Discord, which you can find here:

https://AlignmentLab.ai

Evaluation

We have evaluated OpenOrcaxOpenChat-Preview2-13B on hard reasoning tasks from BigBench-Hard and AGIEval as outlined in the Orca paper.

Our average performance for BigBench-Hard: 0.488

Average for AGIEval: 0.447

In the Orca paper, they measured their score relative to Vicuna on these evals. We have done the same and have found our score averages to ~103% of the total performance that was shown in the Orca paper, using the same evaluation methods as outlined in the paper.

So we are surpassing Orca performance with <20% of the dataset size and <1/10th the training budget!

As well, we have evaluated using the methodology and tools for the HuggingFace Leaderboard and GPT4ALL Leaderboard, and find that we place #1 on both for all 13B models at release time!

AGIEval Performance

We present our results in two columns. The column for "(Orca Paper eval)" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper. The column for "(HF Leaderboard eval)" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.

OpenOrca Preview2 AGIEval Performance

BigBench-Hard Performance

We present our results in two columns. The column for "(Orca Paper eval)" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper. The column for "(HF Leaderboard eval)" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.

OpenOrca Preview2 BigBench-Hard Performance

HuggingFaceH4 Open LLM Leaderboard Performance

We have run our own tests using parameters matching the HuggingFaceH4 Open LLM Leaderboard evals.

We place #1 for all 13B models at release time!

OpenOrca Preview2 HuggingFace Leaderboard Performance

GPT4ALL Leaderboard Performance

We have tested using parameters matching the GPT4ALL Benchmark Suite and report our results and placement vs their official reporting below.

We place #1 for all open models and come within comparison of text-davinci-003, a proprietary OpenAI model an order of magnitude larger.

OpenOrca Preview2 GPT4ALL Performance

Dataset

We used a curated, filtered selection of most of the GPT-4 augmented data from our OpenOrca dataset, which aims to reproduce the Orca Research Paper dataset. Further details of our curation practices will be forthcoming with our full model releases.

Training

We trained with 8x A100-80G GPUs for 46 hours, completing 5 epochs of full fine tuning on our dataset in one training run. This contrasts with the 20x A100-80G GPUs for 200 hours used in the Orca paper, for only 3 epochs, and requiring stacked training (which is known to suffer catastrophic forgetting). Our compute requirement was <1/10th that of the original Orca. Commodity cost was ~$600.

Please await our full releases for further training details.

Prompt Template

We use our own prompt template which we call "OpenChat Llama2 V1"

Examples:

# Single-turn V1 Llama 2
tokenize("User: Hello<|end_of_turn|>Assistant:")
# Result: [1, 4911, 29901, 15043, 32000, 4007, 22137, 29901]

# Multi-turn V1 Llama 2
tokenize("User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
# Result: [1, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]

For UIs with Prefix and Suffix fields, these will likely work:

Prefix (include a space after colon):

User:

Suffix (space after colon):

<|end_of_turn|>\nAssistant:

Serving

This model is most easily served with OpenChat's customized vLLM OpenAI-compatible API server. This is highly recommended as it is by far the fastest in terms of inference speed and is a quick and easy option for setup. We also illustrate setup of Oobabooga/text-generation-webui below. The settings outlined there will also apply to other uses of Transformers.

Serving with OpenChat

Install OpenChat

After installation, run:

python -m ochat.serving.openai_api_server \
  --model-type openchat_llama2 \
  --model Open-Orca/OpenOrcaxOpenChat-Preview2-13B \
  --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120

Follow the OpenChat documentation to use features such as tensor parallelism on consumer GPUs, API keys, and logging. You may then connect to the OpenAI-compatible API endpoint with tools such as BetterGPT.chat.

Serving with Oobabooga / text-generation-webui

The model may also be loaded via oobabooga/text-generation-webui in a similar manner to other models. See the requirements below. Note that inference with Transformers is significantly slower than using the recommended OpenChat vLLM server.

Oobabooga Key Requirements

  • You will first need to download the model as you normally do to the "models/" folder of your text-generation-webui installation.
  • To use the unquantized model presented here, select "Transformers"" in the webui's "Model" tab "Model loader" dropdown.
    • You will likely want to tick "auto-devices". The model will require >40GB VRAM after loading in context for inference.
    • The model was trained in bf16, so tick the "bf16" box for best performance.
    • It will run safely on single GPUs with VRAM >=48GB (e.g. A6000)
      • If using consumer GPUs, e.g. 2x RTX3090 24GB, you will likely want to enter "18,17" under "tensor_split" to split the model across both GPUs
  • The model will perform significantly better if you use the appropriate prompting template
    • We will submit a PR to include our prompting template into text-generation-webui soon
    • For now, manually enter the settings described in the following sections:

Oobabooga Chat Settings

In the "Chat settings" tab, select the following settings:

For "User String" ...

User:

For "Bot string" ...

Assistant:

For "Context", it is not necessary but we have found good results with ...

You are a helpful assistant. Please answer truthfully and write out your thinking step by step to be sure you get the right answer. If you make a mistake or encounter an error in your thinking, say so out loud and attempt to correct it. If you don't know or aren't sure about something, say so clearly. You will act as a professional logician, mathematician, and physicist. You will also act as the most appropriate type of expert to answer any particular question or solve the relevant problem; state which expert type your are, if so. Also think of any particular named expert that would be ideal to answer the relevant question or solve the relevant problem; name and act as them, if appropriate.

For "Turn template", this is absolutely essential to have. You will get poor, mixed up output without this template ...

<|user|> <|user-message|><|end_of_turn|>\n<|bot|> <|bot-message|>\n

When done, it should look as below:

You may then save this as a named template preset by clicking the "Floppy" icon and giving it an appropriate name in the popup, e.g. "OpenOrcaxOpenChat Llama2".

Oobabooga Text Generation Mode

In the "Text generation" tab, select "instruct" as the mode:

Mode Illustration

It should look as below:

Then you should be ready to generate!

Citation 

@software{OpenOrcaxOpenChatPreview2,
  title = {OpenOrcaxOpenChatPreview2: Llama2-13B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
  author = {Guan Wang and Bleys Goodson and Wing Lian and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B},
}
@software{openchat,
  title = {{OpenChat: Advancing Open-source Language Models with Imperfect Data}},
  author = {Wang, Guan and Cheng, Sijie and Yu, Qiying and Liu, Changling},
  doi = {10.5281/zenodo.8105775},
  url = {https://github.com/imoneoi/openchat},
  version = {pre-release},
  year = {2023},
  month = {7},
}
@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{longpre2023flan,
      title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, 
      author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
      year={2023},
      eprint={2301.13688},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
@software{touvron2023llama,
  title={LLaMA: Open and Efficient Foundation Language Models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}
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