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orca_mini_v3_7b

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


orca-mini


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

πŸ” 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! πŸ˜‰

Check it out here πŸ‘‰

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}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 47.98
ARC (25-shot) 56.91
HellaSwag (10-shot) 79.64
MMLU (5-shot) 52.37
TruthfulQA (0-shot) 50.51
Winogrande (5-shot) 74.27
GSM8K (5-shot) 7.13
DROP (3-shot) 15.06

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 53.47
AI2 Reasoning Challenge (25-Shot) 56.91
HellaSwag (10-Shot) 79.64
MMLU (5-Shot) 52.37
TruthfulQA (0-shot) 50.51
Winogrande (5-shot) 74.27
GSM8k (5-shot) 7.13
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Datasets used to train pankajmathur/orca_mini_v3_7b

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Evaluation results