language, license, library_name, datasets, model-index

language	license	library_name	datasets	model-index
en	llama2	transformers	pankajmathur/orca_mini_v1_dataset pankajmathur/dolly-v2_orca pankajmathur/WizardLM_Orca pankajmathur/alpaca_orca ehartford/dolphin	name results model_009 task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 71.59 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 87.7 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 69.43 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 60.72 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 82.32 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 39.42 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/model_009 Open LLM Leaderboard

model_009

A Llama2-70b model trained on Orca Style datasets.

"Obsessed with GenAI's potential? So am I ! Let's create together 🚀 https://www.linkedin.com/in/pankajam"

Evaluation

We evaluated model_009 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
ARC	0.7159
HellaSwag	0.8771
MMLU	0.6943
TruthfulQA	0.6072
Winogrande	0.8232
GSM8k	0.3942
DROP	0.4401
Total Average	0.6503

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:

OobaBooga Instructions:

This model required upto 45GB GPU VRAM in 4bit so it can be loaded directly on Single RTX 6000/L40/A40/A100/H100 GPU or Double RTX 4090/L4/A10/RTX 3090/RTX A5000 So, if you have access to Machine with 45GB GPU VRAM and have installed OobaBooga Web UI on it. You can just download this model by using HF repo link directly on OobaBooga Web UI "Model" Tab/Page & Just use load-in-4bit option in it.

After that go to Default Tab/Page on OobaBooga Web UI and copy paste above prompt format into Input and Enjoy!

Code Instructions:

Below shows a code example on how to use this model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/model_009")
model = AutoModelForCausalLM.from_pretrained(
  "pankajmathur/model_009",
  torch_dtype=torch.float16,
  load_in_4bit=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{model_009,
  author = {Pankaj Mathur},
  title = {model_009: An Orca Style Llama2-70b model},
  month = {August},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/pankajmathur/model_009},
}

@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{touvron2023llama2,
  title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
  author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
 Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
  year={2023}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	65.03
ARC (25-shot)	71.59
HellaSwag (10-shot)	87.7
MMLU (5-shot)	69.43
TruthfulQA (0-shot)	60.72
Winogrande (5-shot)	82.32
GSM8K (5-shot)	39.42
DROP (3-shot)	44.01

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.53
AI2 Reasoning Challenge (25-Shot)	71.59
HellaSwag (10-Shot)	87.70
MMLU (5-Shot)	69.43
TruthfulQA (0-shot)	60.72
Winogrande (5-shot)	82.32
GSM8k (5-shot)	39.42