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orca_mini_v3_7b

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orca_mini_v3_7b
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---
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:
Tell me about Orcas.

### 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,
  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}
}
```