perlthoughts_-_Chupacabra-7B-v2-gguf

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Chupacabra-7B-v2 - GGUF

• Model creator: https://huggingface.co/perlthoughts/
• Original model: https://huggingface.co/perlthoughts/Chupacabra-7B-v2/

Original model description:

license: apache-2.0 model-index:

• name: Chupacabra-7B-v2 results:
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 65.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 83.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 63.6 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 57.17 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 78.14 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 54.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B-v2 name: Open LLM Leaderboard

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Chupacabra 7B v2

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

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.

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.

It has not been without challenges. There were skeptics who doubted my abilities and questioned my approach. My approach can be changed, but a closed mind cannot.

I refused to let their negativity bring me down. Instead, I used their doubts as fuel to push myself even harder. I worked tirelessly (vapenation), day and night, until I finally succeeded 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 me the way.

"Hate it or love it, the underdogs on top." - The Game

Here is my contribution.

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.

More info

• Developed by: Ray Hernandez
• Model type: Mistral
• Language(s) (NLP): English
• License: Apache 2.0

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Training Details

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here