Inv_-_Konstanta-Alpha-V2-7B-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Konstanta-Alpha-V2-7B - GGUF

• Model creator: https://huggingface.co/Inv/
• Original model: https://huggingface.co/Inv/Konstanta-Alpha-V2-7B/

Original model description:

language:

• en license: apache-2.0 library_name: transformers tags:
• mergekit
• merge
• mistralai/Mistral-7B-v0.1
• SanjiWatsuki/Kunoichi-DPO-v2-7B
• maywell/PiVoT-0.1-Evil-a
• mlabonne/ArchBeagle-7B
• LakoMoor/Silicon-Alice-7B
• roleplay
• rp
• not-for-all-audiences base_model:
• mistralai/Mistral-7B-v0.1
• SanjiWatsuki/Kunoichi-DPO-v2-7B
• maywell/PiVoT-0.1-Evil-a
• mlabonne/ArchBeagle-7B
• LakoMoor/Silicon-Alice-7B model-index:
• name: Konstanta-Alpha-V2-7B 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: 69.62 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B 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: 87.14 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B 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: 65.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B 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: 61.08 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B 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: 81.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B 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: 69.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-Alpha-V2-7B name: Open LLM Leaderboard

---

Konstanta-Alpha-V2-7B

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES to merge Kunoichi with PiVoT Evil and to merge ArchBeagle with Silicon Alice, and then merge the resulting 2 models with the gradient SLERP merge method. ChatML seems to work the best.

Models Merged

The following models were included in the merge:

• SanjiWatsuki/Kunoichi-DPO-v2-7B
• maywell/PiVoT-0.1-Evil-a
• mlabonne/ArchBeagle-7B
• LakoMoor/Silicon-Alice-7B

Configuration

The following YAML configuration was used to produce this model (to reproduce use mergekit-mega command):

base_model: mistralai/Mistral-7B-v0.1
dtype: float16
merge_method: dare_ties
parameters:
  int8_mask: true
slices:
- sources:
  - layer_range: [0, 32]
    model: mistralai/Mistral-7B-v0.1
  - layer_range: [0, 32]
    model: : SanjiWatsuki/Kunoichi-DPO-v2-7B
    parameters:
      density: 0.8
      weight: 0.5
  - layer_range: [0, 32]
    model: : maywell/PiVoT-0.1-Evil-a
    parameters:
      density: 0.3
      weight: 0.15
name: first-step
---
base_model: mistralai/Mistral-7B-v0.1
dtype: float16
merge_method: dare_ties
parameters:
  int8_mask: true
slices:
- sources:
  - layer_range: [0, 32]
    model: mistralai/Mistral-7B-v0.1
  - layer_range: [0, 32]
    model: mlabonne/ArchBeagle-7B
    parameters:
      density: 0.8
      weight: 0.75
  - layer_range: [0, 32]
    model: LakoMoor/Silicon-Alice-7B
    parameters:
      density: 0.6
      weight: 0.30
name: second-step
---
models:
   - model: first-step
   - model: second-step
merge_method: slerp
base_model: first-step
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
  int8_mask: true
  normalize: true
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here