maldv_-_winter-garden-7b-alpha-gguf

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winter-garden-7b-alpha - GGUF

• Model creator: https://huggingface.co/maldv/
• Original model: https://huggingface.co/maldv/winter-garden-7b-alpha/

Original model description:

license: cc-by-nc-4.0 tags:

• merge
• conversational
• multi-task pipeline_tag: text-generation base_model:
• paulml/OmniBeagleSquaredMBX-v3-7B
• ZySec-AI/ZySec-7B-v1
• liminerity/Omningotex-7b-slerp
• localfultonextractor/Erosumika-7B
• KatyTheCutie/LemonadeRP-4.5.3
• cgato/Thespis-Krangled-7b
• CorticalStack/pastiche-crown-clown-7b-dare
• snorkelai/Snorkel-Mistral-PairRM-DPO
• MTSAIR/multi_verse_model model-index:
• name: winter-garden-7b-alpha - "Smart Assistant" 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=maldv/winter-garden-7b-alpha 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: 85.36 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha 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: 65.2 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha 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: 50.94 source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha 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: 80.35 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha 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: 54.44 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=maldv/winter-garden-7b-alpha name: Open LLM Leaderboard

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Winter Garden 7B - α - "Smart Assistant"

It was mentioned that we are in the open ai dark winter; so I thought I would make myself a nice winter garden.

An experiment

I've merged four partitions successfully in the past, so lets go for 9! I started with:

• Mistral-7B-v0.1

and merged in

• OmniBeagleSquaredMBX-v3-7B
• ZySec-7B-v1
• Omningotex-7b-slerp
• Erosumika-7B
• LemonadeRP-4.5.3
• Thespis-Krangled-7b
• pastiche-crown-clown-7b-dare
• Snorkel-Mistral-PairRM-DPO
• multi_verse_model

9-partition merge

All of the layers were partitioned in to 9 random bins. Alternating models were slerped at [0...1], and [1...0] gradients; except attention, which was slerped at 0.03.

This means that the model is still predominantly ordered around base mistral - including half of the input and output layers, and 28% of attention.

Other

Includes fast tokenizer.

Chat Template

I put a conversational chat template, which takes "name", "to" (optional), and "content" as the turns. It is designed to follow a transcript style chat which is used by some of the models. This type of use-case is best done by outlining a scene and creating a character card.

### {% title %}
{% metadata %}

USER: Hello

ASSISTANT: Hi, how are you?

It leans to being a coder when given an ### Instruction, follows <s>[INST][/INST], and likes <|user|>, <|assistant|> as well.

A quite cheery and intelligent model. Very good with science and math, but still capable of a decent amount of creativity for a 7b model.

Scores

Details