Text Generation
Transformers
PyTorch
English
llama
Eval Results
text-generation-inference
Inference Endpoints
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@@ -13,7 +13,22 @@ datasets:
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  An **Uncensored** LLaMA-7b model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.
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- Please note in addition to logical thinking, this model has *better code generation capabilites* compare to our original orca_mini_7b was trained on base OpenLLaMA-7b model, which has the whitespace issues & found not good for code generation.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Dataset
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@@ -101,8 +116,10 @@ print(generate_text(system, instruction))
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  ```
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- ```
 
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  [!] Response:
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  Breaking into your own car requires certain skills and tools. Here are the basic steps:
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@@ -113,9 +130,6 @@ Breaking into your own car requires certain skills and tools. Here are the basic
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  5. If the ^^^^^^^^^^^^^.
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  ```
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-
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- **P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at www.linkedin.com/in/pankajam**
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-
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  **
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  Next Goals:
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  If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX:
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  ```
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- @misc{wizardlm_alpaca_dolly_orca_open_llama_7b,
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  author = {Pankaj Mathur},
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- title = {wizardlm_alpaca_dolly_orca_open_llama_7b: An explain tuned OpenLLaMA-7b model on custom wizardlm, alpaca, & dolly datasets},
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  year = {2023},
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  publisher = {GitHub, HuggingFace},
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  journal = {GitHub repository, HuggingFace repository},
@@ -150,12 +164,11 @@ If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or
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  }
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  ```
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  ```
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- @software{openlm2023openllama,
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- author = {Xinyang Geng and Hao Liu},
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- title = {OpenLLaMA: An Open Reproduction of LLaMA},
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- month = May,
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- year = 2023,
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- url = {https://github.com/openlm-research/open_llama}
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  }
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  ```
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  ```
@@ -177,4 +190,23 @@ If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or
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  journal = {GitHub repository},
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  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
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  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  An **Uncensored** LLaMA-7b model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.
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+ Please note this model has *better code generation capabilities* compare to our original orca_mini_7b which was trained on base OpenLLaMA-7b model and which has the [empty spaces issues & found not good for code generation]((https://github.com/openlm-research/open_llama#update-06072023)).
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+
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+
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+ **P.S. I am #opentowork, if you can help, please reach out to me at www.linkedin.com/in/pankajam**
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+
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+ # Evaluation
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+
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+ |||||||
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+ |:------:|:-------------:|:---------:|:--------:|:-------:|:--------:|
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+ |**Task**|**num_fewshot**|**Version**|**Metric**|**Value**|**Stderr**|
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+ |*arc_easy*|0|0|acc|0.7386|0.0090|
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+ |*arc_easy*|0|0|acc_norm|0.7066|0.0093|
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+ |*hellaswag*|0|0|acc|0.5591|0.0050|
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+ |*hellaswag*|0|0|acc_norm|0.7394|0.0044|
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+ |*truthfulqa_mc*|0|1|mc1|0.2938|0.0159|
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+ |*truthfulqa_mc*|0|1|mc2|0.4399|0.0153|
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  # Dataset
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  ```
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+ **NOTE: The real response is hided here with ^^^^^^^^^^^^^.**
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+ *Try on your own private machine to see uncensored responses*
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+ ```
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  [!] Response:
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  Breaking into your own car requires certain skills and tools. Here are the basic steps:
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  5. If the ^^^^^^^^^^^^^.
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  ```
 
 
 
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  **
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  Next Goals:
 
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  If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX:
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  ```
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+ @misc{orca_mini_v2_7b,
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  author = {Pankaj Mathur},
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+ title = {orca_mini_v2_7b: An explain tuned LLaMA-7b model on uncensored wizardlm, alpaca, & dolly datasets},
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  year = {2023},
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  publisher = {GitHub, HuggingFace},
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  journal = {GitHub repository, HuggingFace repository},
 
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  }
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  ```
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  ```
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+ @software{touvron2023llama,
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+ title={LLaMA: Open and Efficient Foundation Language Models},
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+ 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},
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+ journal={arXiv preprint arXiv:2302.13971},
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+ year={2023}
 
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  }
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  ```
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  ```
 
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  journal = {GitHub repository},
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  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
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  }
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+ ```
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+ ```
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+ @online{DatabricksBlog2023DollyV2,
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+ author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
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+ title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
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+ year = {2023},
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+ url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
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+ urldate = {2023-06-30}
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+ }
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+ ```
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+ ```
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+ @misc{xu2023wizardlm,
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+ title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
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+ author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
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+ year={2023},
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+ eprint={2304.12244},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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  ```