perlthoughts/Chupacabra-7B-v2.04

Chupacabra 7B v2.04

Model Description

SLERP merge.

Mistral Instruct v0.2 merge test.

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 {prompt} with your prompt instruction.

[INST] {prompt} [/INST] 

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

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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.

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

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

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.52
AI2 Reasoning Challenge (25-Shot)	66.30
HellaSwag (10-Shot)	85.70
MMLU (5-Shot)	60.94
TruthfulQA (0-shot)	67.76
Winogrande (5-shot)	78.93
GSM8k (5-shot)	51.48