metadata
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
- senseable/WestLake-7B-v2
- mlabonne/OmniBeagle-7B
- vanillaOVO/supermario_v3
base_model:
- jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
- senseable/WestLake-7B-v2
- mlabonne/OmniBeagle-7B
- vanillaOVO/supermario_v3
MixtureofMerges-MoE-4x7b-v3
MixtureofMerges-MoE-4x7b-v3 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
- jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
- senseable/WestLake-7B-v2
- mlabonne/OmniBeagle-7B
- vanillaOVO/supermario_v3
🧩 Configuration
base_model: senseable/WestLake-7B-v2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
positive_prompts:
- "Answer this question from the ARC (Argument Reasoning Comprehension)."
- "Use common sense and logical reasoning skills."
negative_prompts:
- "nonsense"
- "irrational"
- "math"
- "code"
- source_model: senseable/WestLake-7B-v2
positive_prompts:
- "Answer this question from the Winogrande test."
- "Use advanced knowledge of culture and humanity"
negative_prompts:
- "ignorance"
- "uninformed"
- "creativity"
- source_model: mlabonne/OmniBeagle-7B
positive_prompts:
- "Calculate the answer to this math problem"
- "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
- "solve for"
negative_prompts:
- "incorrect"
- "inaccurate"
- "creativity"
- source_model: vanillaOVO/supermario_v3
positive_prompts:
- "Predict the most plausible continuation for this scenario."
- "Demonstrate understanding of everyday commonsense in your response."
- "Use contextual clues to determine the most likely outcome."
- "Apply logical reasoning to complete the given narrative."
- "Infer the most realistic action or event that follows."
negative_prompts:
- "guesswork"
- "irrelevant information"
- "contradictory response"
- "illogical conclusion"
- "ignoring context"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MixtureofMerges-MoE-4x7b-v3"
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"])