---
license: apache-2.0
---

# Chupacabra 7B v2.01 (GGUF)

<p><img src="https://huggingface.co/perlthoughts/Chupacabra-7B/resolve/main/chupacabra7b%202.png" width=330></p>

### 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]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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### Results

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#### Summary


## Model Examination [optional]

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Software

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## Glossary [optional]

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{"license": "apache-2.0"}
gguf license:apache-2.0 region:us