orca_mini_v2_7b

An Uncensored LLaMA-7b 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_7b which was trained on base OpenLLaMA-7b 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_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	0.5077	0.0146
hellaswag	acc_norm	0.7617	0.0043
mmlu	acc_norm	0.3955	0.035
truthfulqa_mc	mc2	0.4399	0.0153
Total Average	-	0.5262	0.0173

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 8x A100(80G) GPUs and lasts for around 13 Hours for cost of $195 using RunPods

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 OpenAlpaca repo

Here are some of params used during training:

batch_size	96
train_micro_batch_size_per_gpu	3
gradient_accumulation_steps	4
Learning rate	2e-5
Max length	1024
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_7b'
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 this model useful in your research or applications, please kindly cite using the following BibTeX:

@misc{orca_mini_v2_7b,
  author = {Pankaj Mathur},
  title = {orca_mini_v2_7b: An explain tuned LLaMA-7b 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_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={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}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	44.24
ARC (25-shot)	50.77
HellaSwag (10-shot)	76.02
MMLU (5-shot)	39.5
TruthfulQA (0-shot)	43.86
Winogrande (5-shot)	71.43
GSM8K (5-shot)	2.88
DROP (3-shot)	25.23

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	47.41
AI2 Reasoning Challenge (25-Shot)	50.77
HellaSwag (10-Shot)	76.02
MMLU (5-Shot)	39.50
TruthfulQA (0-shot)	43.86
Winogrande (5-shot)	71.43
GSM8k (5-shot)	2.88