đ Chupacabra 7B v2
Chupacabra 7B v2 is a merged model based on Mistral, aiming to achieve excellent text - generation performance through advanced merging and training methods.
đ Quick Start
Use the code below to get started with the model.
[More Information Needed]
⨠Features
Model Description
This model was made by merging models based on Mistral with the SLERP merge method.
Advantages of SLERP vs averaging weights(common) are as follows:
- Spherical Linear Interpolation (SLERP) - Traditionally, model merging often resorts to weight averaging which, although straightforward, might not always capture the intricate features of the models being merged. The SLERP technique addresses this limitation, producing a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents.
- Smooth Transitions - SLERP ensures smoother transitions between model parameters. This is especially significant when interpolating between high - dimensional vectors.
- Better Preservation of Characteristics - Unlike weight averaging, which might dilute distinct features, SLERP preserves the curvature and characteristics of both models in high - dimensional spaces.
- Nuanced Blending - SLERP takes into account the geometric and rotational properties of the models in the vector space, resulting in a blend that is more reflective of both parent models' characteristics.
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), supervised fine - tuning (SFT) and reinforced learning.
The developer spent countless hours studying the latest research papers, attending conferences, and networking with experts in the field. Experimented with different algorithms, tactics, fine - tuned hyperparameters, optimizers, and optimized code until achieving the best possible results.
There were challenges, with skeptics doubting abilities and questioning the approach. But the developer refused to be brought down by their negativity. Instead, used their doubts as fuel to push harder. Worked tirelessly until finally succeeding in merging with the most performant model weights using SOTA training methods like DPO and other advanced techniques described above.
Thank you openchat 3.5 for showing the way.
"Hate it or love it, the underdogs on top." - The Game
Prompt Template
Replace {system} with your system prompt, and {prompt} with your prompt instruction.
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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.
- Fixed issue with tokenizer not stopping correctly and changed prompt template.
- Uploaded new merged model weights.
đĻ Installation
No installation steps provided in the original document.
đģ Usage Examples
No code examples provided in the original document.
đ Documentation
More info
- Developed by: Ray Hernandez
- Model type: Mistral
- Language(s) (NLP): English
- License: Apache 2.0
Model Sources [optional]
Uses
Direct Use
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Downstream Use [optional]
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Out - of - Scope Use
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Bias, Risks, and Limitations
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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.
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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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
Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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đ§ Technical Details
No technical details provided in the original document.
đ License
The model is licensed under the Apache 2.0 license.
Detailed results can be found here
| Property |
Details |
| Avg. |
67.04 |
| AI2 Reasoning Challenge (25 - Shot) |
65.19 |
| HellaSwag (10 - Shot) |
83.39 |
| MMLU (5 - Shot) |
63.60 |
| TruthfulQA (0 - shot) |
57.17 |
| Winogrande (5 - shot) |
78.14 |
| GSM8k (5 - shot) |
54.74 |
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