README.md

metadata

language:
  - en
license: other
library_name: transformers
datasets:
  - psmathur/orca_mini_v1_dataset
  - ehartford/dolphin
model_name: Orca Mini v3 7B
base_model: psmathur/orca_mini_v3_7b
inference: false
model_creator: Pankaj Mathur
model_type: llama
pipeline_tag: text-generation
prompt_template: >
  ### System:

  You are an AI assistant that follows instruction extremely well. Help as much
  as you can.


  ### User:

  {prompt}


  ### Input:

  {input}


  ### Response:
quantized_by: TheBloke

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

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

---

Orca Mini v3 7B - GGUF

• Model creator: Pankaj Mathur
• Original model: Orca Mini v3 7B

Description

This repo contains GGUF format model files for Pankaj Mathur's Orca Mini v3 7B.

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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
• LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• 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

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Pankaj Mathur's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: orca_mini

### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.

### User:
{prompt}

### Input:
{input}

### Response:

Licensing

The creator of the source model has listed its license as other, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: Pankaj Mathur's Orca Mini v3 7B.

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221

They are also compatible with many third party UIs and libraries - please see the list at the top of this 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
orca_mini_v3_7b.Q2_K.gguf	Q2_K	2	2.83 GB	5.33 GB	smallest, significant quality loss - not recommended for most purposes
orca_mini_v3_7b.Q3_K_S.gguf	Q3_K_S	3	2.95 GB	5.45 GB	very small, high quality loss
orca_mini_v3_7b.Q3_K_M.gguf	Q3_K_M	3	3.30 GB	5.80 GB	very small, high quality loss
orca_mini_v3_7b.Q3_K_L.gguf	Q3_K_L	3	3.60 GB	6.10 GB	small, substantial quality loss
orca_mini_v3_7b.Q4_0.gguf	Q4_0	4	3.83 GB	6.33 GB	legacy; small, very high quality loss - prefer using Q3_K_M
orca_mini_v3_7b.Q4_K_S.gguf	Q4_K_S	4	3.86 GB	6.36 GB	small, greater quality loss
orca_mini_v3_7b.Q4_K_M.gguf	Q4_K_M	4	4.08 GB	6.58 GB	medium, balanced quality - recommended
orca_mini_v3_7b.Q5_0.gguf	Q5_0	5	4.65 GB	7.15 GB	legacy; medium, balanced quality - prefer using Q4_K_M
orca_mini_v3_7b.Q5_K_S.gguf	Q5_K_S	5	4.65 GB	7.15 GB	large, low quality loss - recommended
orca_mini_v3_7b.Q5_K_M.gguf	Q5_K_M	5	4.78 GB	7.28 GB	large, very low quality loss - recommended
orca_mini_v3_7b.Q6_K.gguf	Q6_K	6	5.53 GB	8.03 GB	very large, extremely low quality loss
orca_mini_v3_7b.Q8_0.gguf	Q8_0	8	7.16 GB	9.66 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.

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

• LM Studio
• LoLLMS Web UI
• Faraday.dev

In text-generation-webui

Under Download Model, you can enter the model repo: TheBloke/orca_mini_v3_7B-GGUF and below it, a specific filename to download, such as: orca_mini_v3_7b.q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub>=0.17.1

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download TheBloke/orca_mini_v3_7B-GGUF orca_mini_v3_7b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/orca_mini_v3_7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/orca_mini_v3_7B-GGUF orca_mini_v3_7b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows CLI users: Use set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 before running the download command.

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.

./main -ngl 32 -m orca_mini_v3_7b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n### User:\n{prompt}\n\n### Input:\n{input}\n\n### Response:"

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 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/orca_mini_v3_7B-GGUF", model_file="orca_mini_v3_7b.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:

• LangChain + llama-cpp-python
• LangChain + ctransformers

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!

Thanks to Clay from gpus.llm-utils.org!

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.

• Patreon: https://patreon.com/TheBlokeAI
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Pankaj Mathur's Orca Mini v3 7B

orca_mini_v3_7b

A LLama2-7b model trained on Orca Style datasets.

🤔 How good is orca-mini-v3-7b? Do the evaluation results from HuggingFace Open LLM leaderboard translate to real-world use cases?

🔍 Now you can figure it out for yourself!

Introducing the orca-mini chatbot powered by the orca-mini-v3-7b model. Dive in and see how the open source 7b model stacks up in the world of massive language models. 🌍

⏰ Hurry up before I run out of GPU credits! 😉

Check it out here 👉

https://huggingface.co/spaces/psmathur/psmathur-orca_mini_v3_7b

P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.

quantized versions

Big thanks to @TheBloke

1. https://huggingface.co/TheBloke/orca_mini_v3_7B-GGML

2. https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ

license disclaimer:

This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.

evaluation

We evaluated orca_mini_v3_7b on a wide range of tasks using Language Model Evaluation Harness from EleutherAI.

Here are the results on metrics used by HuggingFaceH4 Open LLM Leaderboard

Task	Metric	Value	Stderr
arc_challenge	acc_norm	0.5717	0.0145
hellaswag	acc_norm	0.7966	0.0043
mmlu	acc_norm	0.5234	0.035
truthfulqa_mc	mc2	0.5029	0.0156
Total Average	-	0.59865

example esage

Here is prompt format

### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.

### User:
Tell me about Orcas.

### Assistant:

Below shows a code example on how to use this model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_7b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
  "psmathur/orca_mini_v3_7b",
  torch_dtype=torch.float16,
  load_in_8bit=True,
  low_cpu_mem_usage=True,
  device_map="auto"
)
system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"

#generate text steps
instruction = "Tell me about Orcas."
prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n"
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=4096)

print(tokenizer.decode(output[0], skip_special_tokens=True))

limitations & biases:

While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.

Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.

Exercise caution and cross-check information when necessary.

citiation:

Please kindly cite using the following BibTeX:

@misc{orca_mini_v3_7b,
  author = {Pankaj Mathur},
  title = {orca_mini_v3_7b: An explain tuned Llama2-7b model},
  year = {2023},
  publisher = {GitHub, HuggingFace},
  journal = {GitHub repository, HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_7b},
}

@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}
}

@software{touvron2023llama,
  title={LLaMA2: 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}
}