Create README.md

README.md

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+---
+datasets:
+- psmathur/orca_minis_uncensored_dataset
+language:
+- en
+library_name: transformers
+---
+
+# orca_mini_v3_13b
+
+A Llama2-13b 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_13b 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 the 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_13b", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("psmathur/orca_mini_v3_13b", 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 = "I want to build the best Large Language Model, Give me detail step by step instructions on how to do it?"
+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_13b,
+  author = {Pankaj Mathur},
+  title = {orca_mini_v3_13b: An explain tuned Llama2-13b model},
+  year = {2023},
+  publisher = {GitHub, HuggingFace},
+  journal = {GitHub repository, HuggingFace repository},
+  howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_13b},
+}
+```
+
+```
+@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={LLaMA: 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}
+}
+```