Update README.md

README.md

@@ -3,6 +3,194 @@ language:
 - en
 library_name: transformers
 license: llama2
+datasets:
+- pankajmathur/orca_mini_v1_dataset
+- pankajmathur/lima_unchained_v1
+- pankajmathur/WizardLM_Orca
+- pankajmathur/alpaca_orca
+- pankajmathur/dolly-v2_orca
+- garage-bAInd/Open-Platypus
+- ehartford/dolphin
 ---
 
-LlaMA-2 License, more details coming soon...
+# model_101
+
+A hybrid (explain + instruct) style Llama2-70b model, Pleae check examples below for both style prompts, Here is the list of datasets used:
+
+* Open-Platypus
+* Alpaca
+* WizardLM
+* Dolly-V2
+* Dolphin Samples (~200K)
+* Orca_minis_v1
+* Alpaca_orca
+* WizardLM_orca
+* Dolly-V2_orca
+
+
+<br>
+
+**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.**
+
+<br>
+
+
+
+### quantized versions
+Coming Soon....
+
+<br>
+
+#### license disclaimer:
+
+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.
+
+<br>
+
+## Evaluation
+
+We evaluated model_001 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|0.7108|0.0141|
+|*hellaswag*|acc_norm|0.8765|0.0038|
+|*mmlu*|acc_norm|0.6904|0.0351|
+|*truthfulqa_mc*|mc2|0.6312|0.0157|
+|**Total Average**|-|**0.72729**||
+
+
+<br>
+
+## Example Usage
+
+Here is the Orca 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/model_007")
+model = AutoModelForCausalLM.from_pretrained(
+  "psmathur/model_007",
+  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))
+
+```
+
+
+Here is the Alpaca prompt format
+
+```
+
+### User:
+Tell me about Alpacas.
+
+### 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("pankajmathur/model_001")
+model = AutoModelForCausalLM.from_pretrained(
+  "pankajmathur/model_007",
+  torch_dtype=torch.float16,
+  load_in_8bit=True,
+  low_cpu_mem_usage=True,
+  device_map="auto"
+)
+#generate text steps
+instruction = "Tell me about Alpacas."
+prompt = f"### 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))
+
+```
+
+<br>
+
+#### 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.
+
+
+<br>
+
+### Citiation:
+
+Please kindly cite using the following BibTeX:
+
+```
+@misc{model_001,
+  author = {Pankaj Mathur},
+  title = {model_001: A hybrid (explain + instruct) style Llama2-70b model},
+  year = {2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://https://huggingface.co/pankajmathur/model_001},
+}
+```
+
+```
+@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}
+}
+```