MaxiCPM-3x3B-Test

MaxiCPM-3x3B-Test is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• indischepartij/MiniCPM-3B-Hercules-v2.0
• indischepartij/MiniCPM-3B-OpenHermes-2.5-v2
• indischepartij/MiniCPM-3B-Bacchus

🧩 Configuration

base_model: openbmb/MiniCPM-2B-dpo-bf16-llama-format
experts:
  - source_model: indischepartij/MiniCPM-3B-Hercules-v2.0
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
  - source_model: indischepartij/MiniCPM-3B-OpenHermes-2.5-v2
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: indischepartij/MiniCPM-3B-Bacchus
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"
dtype: bfloat16

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "gmonsoon/MaxiCPM-3x3B-Test"

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.	53.90
AI2 Reasoning Challenge (25-Shot)	45.99
HellaSwag (10-Shot)	71.74
MMLU (5-Shot)	52.88
TruthfulQA (0-shot)	41.06
Winogrande (5-shot)	66.85
GSM8k (5-shot)	44.88