OLMoE

This model was released on 2024-09-03 and added to Hugging Face Transformers on 2024-09-03.

OLMoE

OLMoE is a sparse Mixture-of-Experts (MoE) language model with 7B parameters but only 1B parameters are used per input token. It has similar inference costs as dense models but trains ~3x faster. OLMoE uses fine-grained routing with 64 small experts in each layer and uses a dropless token-based routing algorithm.

You can find all the original OLMoE checkpoints under the OLMoE collection.

Tip

This model was contributed by Muennighoff.

Click on the OLMoE models in the right sidebar for more examples of how to apply OLMoE to different language tasks.

The example below demonstrates how to generate text with Pipeline or the AutoModel class.

import torch
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="allenai/OLMoE-1B-7B-0125",
    dtype=torch.float16,
    device=0,
)

result = pipe("Dionysus is the god of")
print(result)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from accelerate import Accelerator

device = Accelerator().device

model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", attn_implementation="sdpa", dtype="auto", device_map="auto").to(device)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")

inputs = tokenizer("Bitcoin is", return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
output = model.generate(**inputs, max_length=64)
print(tokenizer.decode(output[0]))

Quantization

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends. The example below uses bitsandbytes to only quantize the weights to 4-bits.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from accelerate import Accelerator

device = Accelerator().device

quantization_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_compute_dtype=torch.float16,
   bnb_4bit_use_double_quant=True,
   bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", attn_implementation="sdpa", dtype="auto", device_map="auto", quantization_config=quantization_config).to(device)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")

inputs = tokenizer("Bitcoin is", return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
output = model.generate(**inputs, max_length=64)
print(tokenizer.decode(output[0]))

OlmoeConfig

[[autodoc]] OlmoeConfig

OlmoeModel

[[autodoc]] OlmoeModel - forward

OlmoeForCausalLM

[[autodoc]] OlmoeForCausalLM - forward