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--- |
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language: |
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- en |
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library_name: transformers |
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license: other |
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datasets: |
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- psmathur/orca_mini_v1_dataset |
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- ehartford/dolphin |
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pipeline_tag: text-generation |
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--- |
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# orca_mini_v3_70b |
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A Llama2-70b model trained on Orca Style datasets. |
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**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.** |
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### quantized versions |
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Big thanks to [@TheBloke](https://huggingface.co/TheBloke) |
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1) https://huggingface.co/TheBloke/orca_mini_v3_70B-GGML |
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2) https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ |
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<br> |
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#### license disclaimer: |
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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. |
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## Evaluation |
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We evaluated orca_mini_v3_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. |
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Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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|**Task**|**Value**| |
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|*ARC*|0.7125| |
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|*HellaSwag*|0.8785| |
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|*MMLU*|0.7018| |
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|*TruthfulQA*|0.6127| |
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|*Winogrande*|0.8272| |
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|*GSM8K*|0.4086| |
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|*DROP*|0.4017| |
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|**Total Average**|**0.649**| |
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<br> |
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### Prompt Format |
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``` |
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### System: |
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You are an AI assistant that follows instruction extremely well. Help as much as you can. |
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### User: |
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Tell me about Orcas. |
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### Assistant: |
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``` |
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#### OobaBooga Instructions: |
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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 |
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So, if you have access to Machine with 45GB GPU VRAM and have installed [OobaBooga Web UI](https://github.com/oobabooga/text-generation-webui) on it. |
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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. |
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After that go to Default Tab/Page on OobaBooga Web UI and **copy paste above prompt format into Input** and Enjoy! |
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<br> |
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#### Code Instructions: |
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Below shows a code example on how to use this model |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_70b") |
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model = AutoModelForCausalLM.from_pretrained( |
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"psmathur/orca_mini_v3_70b", |
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torch_dtype=torch.float16, |
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load_in_4bit=True, |
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low_cpu_mem_usage=True, |
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device_map="auto" |
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) |
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system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n" |
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#generate text steps |
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instruction = "Tell me about Orcas." |
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prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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<br> |
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#### Limitations & Biases: |
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. |
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. |
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Exercise caution and cross-check information when necessary. |
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<br> |
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### Citiation: |
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Please kindly cite using the following BibTeX: |
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``` |
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@misc{orca_mini_v3_70b, |
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author = {Pankaj Mathur}, |
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title = {orca_mini_v3_70b: An Orca Style Llama2-70b model}, |
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month = {august}, |
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year = {2023}, |
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publisher = {HuggingFace}, |
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journal = {HuggingFace repository}, |
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howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_70b}, |
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} |
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``` |
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``` |
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@misc{mukherjee2023orca, |
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title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, |
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author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, |
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year={2023}, |
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eprint={2306.02707}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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``` |
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@software{touvron2023llama2, |
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title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, |
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author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, |
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Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, |
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Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, |
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Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, |
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Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, |
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Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, |
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Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom}, |
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year={2023} |
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} |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__orca_mini_v3_70b) |
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| Metric | Value | |
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| Avg. | 64.9 | |
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| ARC (25-shot) | 71.25 | |
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| HellaSwag (10-shot) | 87.85 | |
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| MMLU (5-shot) | 70.18 | |
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| TruthfulQA (0-shot) | 61.27 | |
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| Winogrande (5-shot) | 82.72 | |
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| GSM8K (5-shot) | 40.86 | |
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| DROP (3-shot) | 40.17 | |
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