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RichardErkhov/
Gille_-_MoE-StrangeMerges-2x7B-gguf

Name: Gille_-_MoE-StrangeMerges-2x7B-gguf
Availability: InStock

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MoE-StrangeMerges-2x7B - GGUF

Model creator: https://huggingface.co/Gille/
Original model: https://huggingface.co/Gille/MoE-StrangeMerges-2x7B/

Name	Quant method	Size
MoE-StrangeMerges-2x7B.Q2_K.gguf	Q2_K	4.43GB
MoE-StrangeMerges-2x7B.IQ3_XS.gguf	IQ3_XS	4.95GB
MoE-StrangeMerges-2x7B.IQ3_S.gguf	IQ3_S	5.22GB
MoE-StrangeMerges-2x7B.Q3_K_S.gguf	Q3_K_S	5.2GB
MoE-StrangeMerges-2x7B.IQ3_M.gguf	IQ3_M	5.35GB
MoE-StrangeMerges-2x7B.Q3_K.gguf	Q3_K	5.78GB
MoE-StrangeMerges-2x7B.Q3_K_M.gguf	Q3_K_M	5.78GB
MoE-StrangeMerges-2x7B.Q3_K_L.gguf	Q3_K_L	6.27GB
MoE-StrangeMerges-2x7B.IQ4_XS.gguf	IQ4_XS	6.5GB
MoE-StrangeMerges-2x7B.Q4_0.gguf	Q4_0	6.78GB
MoE-StrangeMerges-2x7B.IQ4_NL.gguf	IQ4_NL	6.85GB
MoE-StrangeMerges-2x7B.Q4_K_S.gguf	Q4_K_S	6.84GB
MoE-StrangeMerges-2x7B.Q4_K.gguf	Q4_K	7.25GB
MoE-StrangeMerges-2x7B.Q4_K_M.gguf	Q4_K_M	7.25GB
MoE-StrangeMerges-2x7B.Q4_1.gguf	Q4_1	7.52GB
MoE-StrangeMerges-2x7B.Q5_0.gguf	Q5_0	8.26GB
MoE-StrangeMerges-2x7B.Q5_K_S.gguf	Q5_K_S	8.26GB
MoE-StrangeMerges-2x7B.Q5_K.gguf	Q5_K	8.51GB
MoE-StrangeMerges-2x7B.Q5_K_M.gguf	Q5_K_M	8.51GB
MoE-StrangeMerges-2x7B.Q5_1.gguf	Q5_1	9.01GB
MoE-StrangeMerges-2x7B.Q6_K.gguf	Q6_K	6.19GB
MoE-StrangeMerges-2x7B.Q8_0.gguf	Q8_0	12.75GB

Original model description:

license: apache-2.0 tags:

moe
frankenmoe
merge
mergekit
lazymergekit
Gille/StrangeMerges_9-7B-dare_ties
Gille/StrangeMerges_8-7B-slerp base_model:
Gille/StrangeMerges_9-7B-dare_ties
Gille/StrangeMerges_8-7B-slerp model-index:
name: MoE-StrangeMerges-2x7B 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: 70.82 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B 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: 87.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B 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: 65.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B 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: 65.86 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B 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: 82.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B 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: 67.7 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/MoE-StrangeMerges-2x7B name: Open LLM Leaderboard

MoE-StrangeMerges-2x7B

MoE-StrangeMerges-2x7B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

yaml

base_model: Gille/StrangeMerges_9-7B-dare_ties
gate_mode: cheap_embed
dtype: float16
experts:
  - source_model: Gille/StrangeMerges_9-7B-dare_ties
    positive_prompts: ["science, logic, math"]
  - source_model: Gille/StrangeMerges_8-7B-slerp
    positive_prompts: ["reasoning, numbers, abstract"]

💻 Usage

python

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Gille/MoE-StrangeMerges-2x7B"

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.	73.34
AI2 Reasoning Challenge (25-Shot)	70.82
HellaSwag (10-Shot)	87.83
MMLU (5-Shot)	65.04
TruthfulQA (0-shot)	65.86
Winogrande (5-shot)	82.79
GSM8k (5-shot)	67.70

RichardErkhov/Gille_-_MoE-StrangeMerges-2x7B-gguf