cosmo-8x334M-random-router-random_init

cosmo-8x334M-random-router-random_init is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• yentinglin/cosmo-334M-random-1
• yentinglin/cosmo-334M-random-2
• yentinglin/cosmo-334M-random-3
• yentinglin/cosmo-334M-random-4
• yentinglin/cosmo-334M-random-5
• yentinglin/cosmo-334M-random-6
• yentinglin/cosmo-334M-random-7
• yentinglin/cosmo-334M-random-8

🧩 Configuration

gate_mode: random # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
experts_per_token: 2
base_model: yentinglin/cosmo-334M-random-1
experts:
  - source_model: yentinglin/cosmo-334M-random-1
  - source_model: yentinglin/cosmo-334M-random-2
  - source_model: yentinglin/cosmo-334M-random-3
  - source_model: yentinglin/cosmo-334M-random-4
  - source_model: yentinglin/cosmo-334M-random-5
  - source_model: yentinglin/cosmo-334M-random-6
  - source_model: yentinglin/cosmo-334M-random-7
  - source_model: yentinglin/cosmo-334M-random-8
dtype: bfloat16

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "yentinglin/cosmo-8x334M-random-router-random_init"

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"])