moe-minicpm-x4-base

moe-minicpm-x4-base

moe-minicpm-x4-base is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
• NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
• NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
• NurtureAI/minicpm-2b-sft-bf16-llamafied-16k

🧩 Configuration

base_model: NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
gate_mode: random
experts_per_token: 2
experts:
  - source_model: NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
    positive_prompts: [""]
  - source_model: NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
    positive_prompts: [""]
  - source_model: NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
    positive_prompts: [""]
  - source_model: NurtureAI/minicpm-2b-sft-bf16-llamafied-16k
    positive_prompts: [""]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "babybirdprd/moe-minicpm-x4-base"

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"])