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jsfs11_-_MixtureofMerges-MoE-2x7b-v7-gguf

Name: jsfs11_-_MixtureofMerges-MoE-2x7b-v7-gguf
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MixtureofMerges-MoE-2x7b-v7 - GGUF

Model creator: https://huggingface.co/jsfs11/
Original model: https://huggingface.co/jsfs11/MixtureofMerges-MoE-2x7b-v7/

Name	Quant method	Size
MixtureofMerges-MoE-2x7b-v7.Q2_K.gguf	Q2_K	4.43GB
MixtureofMerges-MoE-2x7b-v7.IQ3_XS.gguf	IQ3_XS	4.95GB
MixtureofMerges-MoE-2x7b-v7.IQ3_S.gguf	IQ3_S	5.22GB
MixtureofMerges-MoE-2x7b-v7.Q3_K_S.gguf	Q3_K_S	5.2GB
MixtureofMerges-MoE-2x7b-v7.IQ3_M.gguf	IQ3_M	5.35GB
MixtureofMerges-MoE-2x7b-v7.Q3_K.gguf	Q3_K	5.78GB
MixtureofMerges-MoE-2x7b-v7.Q3_K_M.gguf	Q3_K_M	5.78GB
MixtureofMerges-MoE-2x7b-v7.Q3_K_L.gguf	Q3_K_L	6.27GB
MixtureofMerges-MoE-2x7b-v7.IQ4_XS.gguf	IQ4_XS	6.5GB
MixtureofMerges-MoE-2x7b-v7.Q4_0.gguf	Q4_0	6.78GB
MixtureofMerges-MoE-2x7b-v7.IQ4_NL.gguf	IQ4_NL	6.85GB
MixtureofMerges-MoE-2x7b-v7.Q4_K_S.gguf	Q4_K_S	6.84GB
MixtureofMerges-MoE-2x7b-v7.Q4_K.gguf	Q4_K	7.25GB
MixtureofMerges-MoE-2x7b-v7.Q4_K_M.gguf	Q4_K_M	7.25GB
MixtureofMerges-MoE-2x7b-v7.Q4_1.gguf	Q4_1	7.52GB
MixtureofMerges-MoE-2x7b-v7.Q5_0.gguf	Q5_0	8.26GB
MixtureofMerges-MoE-2x7b-v7.Q5_K_S.gguf	Q5_K_S	8.26GB
MixtureofMerges-MoE-2x7b-v7.Q5_K.gguf	Q5_K	8.51GB
MixtureofMerges-MoE-2x7b-v7.Q5_K_M.gguf	Q5_K_M	8.51GB
MixtureofMerges-MoE-2x7b-v7.Q5_1.gguf	Q5_1	9.01GB
MixtureofMerges-MoE-2x7b-v7.Q6_K.gguf	Q6_K	9.84GB
MixtureofMerges-MoE-2x7b-v7.Q8_0.gguf	Q8_0	12.75GB

Original model description:

license: apache-2.0 tags:

moe
frankenmoe
merge
mergekit
lazymergekit
Gille/StrangeMerges_32-7B-slerp
mlabonne/AlphaMonarch-7B base_model:
Gille/StrangeMerges_32-7B-slerp
mlabonne/AlphaMonarch-7B model-index:
name: MixtureofMerges-MoE-2x7b-v7 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: 73.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 89.05 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 64.63 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 78.34 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 84.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 69.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 name: Open LLM Leaderboard

MixtureofMerges-MoE-2x7b-v7

MixtureofMerges-MoE-2x7b-v7 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

yaml

base_model: Gille/StrangeMerges_32-7B-slerp
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: Gille/StrangeMerges_32-7B-slerp
    positive_prompts:
      - "Answer this question from the ARC (Argument Reasoning Comprehension)."
      - "Use common sense and logical reasoning skills."
      - "What assumptions does this argument rely on?"
      - "Are these assumptions valid? Explain."
      - "Analyze the logical structure of this argument. Identify the premises, conclusion, and any assumptions made"
      - "Identify any potential counterarguments to this position. How might someone challenge the reasoning presented?"
      - "Could this be explained in a different way? Provide an alternative explanation."
      - "Identify any weaknesses in this argument."
      - "Does this argument contain any logical fallacies? If so, which ones?"
      - "Generate a few possible continuations to this scenario."
      - "Demonstrate understanding of everyday commonsense in your response."
      - "Use contextual clues to determine the most likely outcome."
      - "Continue this scenario, but make the writing style sound archaic and overly formal."
      - "This narrative is predictable. Can you introduce an unexpected yet plausible twist?"
      - "The character is angry. Continue this scenario showcasing a furious outburst."
    negative_prompts:
      - "misses key evidence"
      - "overly general"
      - "commits the fallacy of hasty generalization"
      - "focuses on irrelevant details"
      - "assumes information not provided"
      - "relies on stereotypes"
      - "repetitive phrases"
      - "engages in circular reasoning"
      - "overuse of the same words"
      - "contradicts earlier statements - breaks the internal logic of the scenario"
      - "out of character dialogue"
      - "awkward phrasing - sounds unnatural"
      - "doesn't match the given genre"
  - source_model: mlabonne/AlphaMonarch-7B
    positive_prompts:
      - "Answer this question, demonstrating commonsense understanding and using any relevant general knowledge you may have."
      - "Provide a concise summary of this passage, then explain why the highlighted section is essential to the main idea."
      - "Read these two brief articles presenting different viewpoints on the same topic. List their key arguments and highlight where they disagree."
      - "Paraphrase this statement, changing the emotional tone but keeping the core meaning intact. Example: Rephrase a worried statement in a humorous way"
      - "Create a short analogy that helps illustrate the main concept of this article."
      - "Explain the concept of physics to a high school student. Use analogies and examples to clarify the main ideas."
      - "Calculate the answer to this math problem"
      - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
      - "solve for"
      - "Analyze the given data and identify any patterns or trends. What conclusions can be drawn from this information?"
      - "A store sells apples at $0.50 each. If Emily buys 12 apples, how much does she need to pay?"
      - "Isolate x in the following equation: 2x + 5 = 17"
      - "Solve this equation and show your working."
      - "Explain why you used this formula to solve the problem."
      - "Attempt to divide this number by zero. Explain why this cannot be done."
    negative_prompts:
      - "sounds too basic"
      - "understated"
      - "dismisses important details"
      - "avoids the question's nuance"
      - "skips essential steps in the solution"
      - "takes this statement too literally"
      - "incorrect"
      - "inaccurate"
      - "assumed without proof"
      - "uses jargon without explanation"
      - "rushed calculation"
      - "confuses mathematical concepts"
      - "draws illogical conclusions"
      - "circular reasoning"

💻 Usage

python

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MixtureofMerges-MoE-2x7b-v7"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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

Metric	Value
Avg.	76.54
AI2 Reasoning Challenge (25-Shot)	73.21
HellaSwag (10-Shot)	89.05
MMLU (5-Shot)	64.63
TruthfulQA (0-shot)	78.34
Winogrande (5-shot)	84.93
GSM8k (5-shot)	69.07

RichardErkhov/jsfs11_-_MixtureofMerges-MoE-2x7b-v7-gguf