RichardErkhov/jsfs11_-_MixtureofMerges-MoE-2x7b-SLERPv0.9-gguf

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MixtureofMerges-MoE-2x7b-SLERPv0.9 - GGUF

• —Model creator: https://huggingface.co/jsfs11/
• —Original model: https://huggingface.co/jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9/

Original model description: --- license: apache-2.0 tags:

• —merge
• —mergekit
• —lazymergekit
• —jsfs11/MixtureofMerges-MoE-2x7b-v7
• —jsfs11/MixtureofMerges-MoE-2x7bRP-v8 base_model:
• —jsfs11/MixtureofMerges-MoE-2x7b-v7
• —jsfs11/MixtureofMerges-MoE-2x7bRP-v8 model-index:
• —name: MixtureofMerges-MoE-2x7b-SLERPv0.9 results:
• —task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2arc config: ARC-Challenge split: test args: numfew_shot: 25 metrics:
• —type: accnorm value: 73.12 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: numfewshot: 10 metrics:
• —type: accnorm value: 88.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: numfewshot: 5 metrics:
• —type: acc value: 65.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthfulqa config: multiplechoice split: validation args: numfewshot: 0 metrics:
• —type: mc2 value: 74.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winograndexl split: validation args: numfew_shot: 5 metrics:
• —type: acc value: 83.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: numfewshot: 5 metrics:
• —type: acc value: 69.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9 name: Open LLM Leaderboard ---

MixtureofMerges-MoE-2x7b-SLERPv0.9

MixtureofMerges-MoE-2x7b-SLERPv0.9 is a merge of the following models using LazyMergekit:

• —jsfs11/MixtureofMerges-MoE-2x7b-v7
• —jsfs11/MixtureofMerges-MoE-2x7bRP-v8

🧩 Configuration

slices:
  - sources:
      - model: jsfs11/MixtureofMerges-MoE-2x7b-v7 
        layer_range: [0, 32]
      - model: jsfs11/MixtureofMerges-MoE-2x7bRP-v8
        layer_range: [0, 32]
merge_method: slerp
base_model: jsfs11/MixtureofMerges-MoE-2x7b-v7
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
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MixtureofMerges-MoE-2x7b-SLERPv0.9"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here