# Chupacabra 7B v2.01

### Model Description Dare-ties merge method. List of all models and merging path is coming soon. ## Purpose Merging the "thick"est model weights from mistral models using amazing training methods like direct preference optimization (dpo) and reinforced learning. I have spent countless hours studying the latest research papers, attending conferences, and networking with experts in the field. I experimented with different algorithms, tactics, fine-tuned hyperparameters, optimizers, and optimized code until i achieved the best possible results. Thank you openchat 3.5 for showing me the way. Here is my contribution. ## Prompt Template Replace {system} with your system prompt, and {prompt} with your prompt instruction. ``` ### System: {system} ### User: {prompt} ### Assistant: ``` ### Bug fixes - Fixed issue with generation and the incorrect model weights. Model weights have been corrected and now generation works again. Reuploading GGUF to the GGUF repository as well as the AWQ versions. - **Developed by:** Ray Hernandez - **Model type:** Mistral - **Language(s) (NLP):** English - **License:** Apache 2.0 ### Model Sources [optional] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.01) | Metric |Value| |---------------------------------|----:| |Avg. |70.43| |AI2 Reasoning Challenge (25-Shot)|68.86| |HellaSwag (10-Shot) |86.12| |MMLU (5-Shot) |63.90| |TruthfulQA (0-shot) |63.50| |Winogrande (5-shot) |80.51| |GSM8k (5-shot) |59.67|

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