Open-LLM Benchmark Results:
MixtureofMerges-MoE-4x7b-v4 (As of 12/02/24 PB Score) on Open LLM Leaderboard📑 Average: 76.23 ARC: 72.53 HellaSwag: 88.85 MMLU: 64.53 TruthfulQA: 75.3 Winogrande: 84.85 GSM8K: 71.34
MixtureofMerges-MoE-4x7b-v4
MixtureofMerges-MoE-4x7b-v4 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
- flemmingmiguel/MBX-7B-v3
- Kukedlc/NeuTrixOmniBe-7B-model-remix
- PetroGPT/WestSeverus-7B-DPO
- vanillaOVO/supermario_v4
🧩 Configuration
base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: flemmingmiguel/MBX-7B-v3
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."
- "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?"
negative_prompts:
- "misses key evidence"
- "overly general"
- "focuses on irrelevant details"
- "assumes information not provided"
- "relies on stereotypes"
- source_model: Kukedlc/NeuTrixOmniBe-7B-model-remix
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."
negative_prompts:
- "sounds too basic"
- "understated"
- "dismisses important details"
- "avoids the question's nuance"
- "takes this statement too literally"
- source_model: PetroGPT/WestSeverus-7B-DPO
positive_prompts:
- "Calculate the answer to this math problem"
- "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
- "solve for"
- "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:
- "incorrect"
- "inaccurate"
- "creativity"
- "assumed without proof"
- "rushed calculation"
- "confuses mathematical concepts"
- "draws illogical conclusions"
- "circular reasoning"
- source_model: vanillaOVO/supermario_v4
positive_prompts:
- "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:
- "repetitive phrases"
- "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"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MixtureofMerges-MoE-4x7b-v4"
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"])
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