README.md

metadata

inference: false
license: other

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Pankaj Mathur's Orca Mini v2 13B GPTQ

These files are GPTQ 4bit model files for Pankaj Mathur's Orca Mini v2 13B.

It is the result of quantising to 4bit using AutoGPTQ.

Repositories available

• 4-bit GPTQ models for GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
• 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, if required

### Response:

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

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/orca_mini_v2_13b-GPTQ.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done"
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: orca_mini_v2_13b-GPTQ
7. The model will automatically load, and is now ready for use!
8. 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.

9. 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
import argparse

model_name_or_path = "TheBloke/orca_mini_v2_13b-GPTQ"
model_basename = "orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order"

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)

prompt = "Tell me about AI"
prompt_template=f'''### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.

### User:
prompt

### Input:
input, if required

### 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'])

Provided files

orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

If a Llama model, it will also be supported by ExLlama, which will provide 2x speedup over AutoGPTQ and GPTQ-for-LLaMa.

It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.

• orca_mini_v2_13b-GPTQ-4bit-128g.no-act.order.safetensors
  • Works with AutoGPTQ in CUDA or Triton modes.
  • ExLlama suupports Llama 4-bit GPTQs, and will provide 2x speedup over AutoGPTQ and GPTQ-for-LLaMa.
  • Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode.
  • Works with text-generation-webui, including one-click-installers.
  • Parameters: Groupsize = 128. Act Order / desc_act = False.

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.

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

Special thanks to: Luke from CarbonQuill, Aemon Algiz.

Patreon special mentions: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi.

Thank you to all my generous patrons and donaters!

Original model card: Pankaj Mathur's Orca Mini v2 13B

orca_mini_v2_13b

An Uncensored LLaMA-13b model in collaboration with Eric Hartford. trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.

Please note this model has better code generation capabilities compare to our original orca_mini_13b which was trained on base OpenLLaMA-13b model and which has the empty spaces issues & found not good for code generation.

P.S. I am #opentowork, if you can help, please reach out to me at www.linkedin.com/in/pankajam

Evaluation

I evaluated orca_mini_v2_13b 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	Value	Stderr
arc_challenge	0.5572	0.0145
hellaswag	0.7964	0.0040
mmlu	0.4969	0.035
truthfulqa_mc	0.5231	0.0158
Total Average	0.5933	0.0114

Dataset

We used uncensored script on top of the previous explain tuned datasets we build which are WizardLM dataset ~70K, Alpaca dataset ~52K & Dolly-V2 dataset ~15K created using approaches from Orca Research Paper.

We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.

This helps student model aka this model to learn thought process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).

Please see below example usage how the System prompt is added before each instruction.

Training

The training configurations are provided in the table below.

The training takes on 4x A100(80G) GPUs and lasts for around 21 Hours for cost of $210 (~$10 for Spot Instance) by using Azure Standard_NC96ads_A100_v4.

We used DeepSpeed with fully sharded data parallelism, also know as ZeRO stage 3 by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing FastChat

Here are some of params used during training:

batch_size	48
train_micro_batch_size_per_gpu	3
gradient_accumulation_steps	4
Learning rate	2e-5
Max length	2048
Epochs	3
Optimizer	AdamW

Example Usage

Here is prompt format for Oobabooga Text generation UI

### System:
{system}

### User:
{instruction}

### Input:
{input}

### Response:

Here is sample example:

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

### User:
Tell me how to break into my own car

### Input:

### Response:
Breaking into your own car requires certain skills and tools. Here are the basic steps:

1. Find a ^^^^^^^^^^^^^
2. Unlock the car by using the ^^^^^^^^^^^^^.
3. Use a ^^^^^^^^^^^^^.
4. Once the ^^^^^^^^^^^^^.
5. If the ^^^^^^^^^^^^^.

Below shows a code example on how to use this model

import torch
from transformers import LlamaForCausalLM, LlamaTokenizer

# Hugging Face model_path
model_path = 'psmathur/orca_mini_v2_13b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map='auto',
)


#generate text function
def generate_text(system, instruction, input=None):
    
    if input:
        prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    else:
        prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n"
    
    tokens = tokenizer.encode(prompt)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to('cuda')

    instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50}

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens, 
            max_length=length+instance['generate_len'], 
            use_cache=True, 
            do_sample=True, 
            top_p=instance['top_p'],
            temperature=instance['temperature'],
            top_k=instance['top_k']
        )    
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f'[!] Response: {string}'

# Sample Test Instruction
system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.'
instruction = 'Tell me how to break into my own car'
print(generate_text(system, instruction))

NOTE: The real response is hidden here with ^^^^^^^^^^^^^.

[!] Response:
Breaking into your own car requires certain skills and tools. Here are the basic steps:

1. Find a ^^^^^^^^^^^^^
2. Unlock the car by using the ^^^^^^^^^^^^^.
3. Use a ^^^^^^^^^^^^^.
4. Once the ^^^^^^^^^^^^^.
5. If the ^^^^^^^^^^^^^.

Next Goals:

1. Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions)
2. Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui)
3. Provide 4bit GGML/GPTQ quantized model (may be TheBloke can help here)

Limitations & Biases:

This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Disclaimer:

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.

Citiation:

If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX:

@misc{orca_mini_v2_13b,
  author = {Pankaj Mathur},
  title = {orca_mini_v2_13b: An explain tuned LLaMA-13b model on uncensored wizardlm, alpaca, & dolly datasets},
  year = {2023},
  publisher = {GitHub, HuggingFace},
  journal = {GitHub repository, HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_13b},
}

@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}
}

@misc{openalpaca,
  author = {Yixuan Su and Tian Lan and Deng Cai},
  title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}},
}

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}

@online{DatabricksBlog2023DollyV2,
    author    = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
    title     = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
    year      = {2023},
    url       = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
    urldate   = {2023-06-30}
}

@misc{xu2023wizardlm,
      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
      author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
      year={2023},
      eprint={2304.12244},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}