|
--- |
|
datasets: |
|
- psmathur/orca_minis_uncensored_dataset |
|
language: |
|
- en |
|
library_name: transformers |
|
--- |
|
|
|
# orca_mini_v3_7b |
|
|
|
A LLama2-7b model trained on Orca Style datasets. |
|
|
|
**I am actively seeking sponsorship and partnership opportunities. If you're interested, please connect with me at www.linkedin.com/in/pankajam.** |
|
|
|
## Evaluation |
|
|
|
We evaluated orca_mini_v3_7b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. |
|
|
|
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/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 Usage |
|
|
|
Here is prompt format |
|
|
|
``` |
|
### System: |
|
You are an AI assistant that follows instruction extremely well. Help as much as you can. |
|
|
|
### User: |
|
I want to build the best Large Language Model, Give me detail step by step instructions on how to do it? |
|
|
|
### Assistant: |
|
|
|
``` |
|
|
|
Below shows a code example on how to use this model |
|
|
|
```python |
|
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, 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} |
|
} |
|
``` |
