metadata

license: apache-2.0
language:
  - en
tags:
  - moe
  - olmo
  - olmoe
co2_eq_emissions: 1
datasets:
  - allenai/ultrafeedback_binarized_cleaned
base_model: allenai/OLMoE-1B-7B-0924-SFT

Model Summary

OLMoE-1B-7B-Instruct is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924) that has been adapted via SFT and DPO from OLMoE-1B-7B. It yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B-Chat. OLMoE is 100% open-source.

This information and more can also be found on the OLMoE GitHub repository.

Paper: (Soon)
Pretraining Checkpoints, Code, Data and Logs.
SFT (Supervised Fine-Tuning) Checkpoints, Code, Data and Logs.
DPO/KTO (Direct Preference Optimization/Kahneman-Tversky Optimization), Checkpoints, Preference Data, DPO code, KTO code and Logs.

Use

Install transformers from source until a release after this PR & torch and run:

from transformers import OlmoeForCausalLM, AutoTokenizer
import torch

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load different ckpts via passing e.g. `revision=kto`
model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924-Instruct").to(DEVICE)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924-Instruct")
messages = [{"role": "user", "content": "Explain to me like I'm five what is Bitcoin."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(DEVICE)
out = model.generate(inputs, max_length=100)
print(tokenizer.decode(out[0]))
"""
<|endoftext|><|user|>
Explain to me like I'm five what is Bitcoin.
<|assistant|>
Bitcoin is like a special kind of money that you can use to buy things online. But unlike regular money, like dollars or euros, Bitcoin isn't printed by governments or banks. Instead, it's created by a special computer program that helps people keep track of it.

Here's how it works: imagine you have a bunch of toys, and you want to
"""

Branches:

main: Preference tuned via DPO model of https://hf.co/OLMoE/OLMoE-1B-7B-0924-SFT (main branch)
load-balancing: Ablation with load balancing loss during DPO starting from the load-balancing branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT
non-annealed: Ablation starting from the non-annealed branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT which is an SFT of the pretraining checkpoint prior to annealing (branch step1200000-tokens5033B of https://hf.co/allenai/OLMoE-1B-7B-0924)
kto: Ablation using KTO instead of DPO. This branch is the checkpoint after 5,000 steps with the RMS optimizer. The other kto* branches correspond to the other checkpoints mentioned in the paper.

Evaluation Snapshot

Task (→)	MMLU	GSM8k	BBH	Human-Eval	Alpaca-Eval 1.0	XSTest	IFEval	Avg
Setup (→)	0-shot	8-shot CoT	3-shot	0-shot	0-shot	0-shot	0-shot
Metric (→)	EM	EM	EM	Pass@10	%win	F1	Loose Acc

OLMo-1B (0724)	25.0	7.0	22.5	16.0	-	67.6	20.5	-
+SFT	36.0	12.5	27.2	21.2	41.5	81.9	26.1	35.9
+DPO	36.7	12.5	30.6	22.0	50.9	79.8	24.2	37.4
OLMo-7B (0724)	50.8	32.5	36.9	32.3	-	80.8	19.6	-
+SFT	54.2	25.0	35.7	38.5	70.9	86.1	39.7	49.3
+DPO	52.8	9.0	16.6	35.0	83.5	87.5	37.9	49.1
JetMoE-2B-9B	45.6	43.0	37.2	54.6	-	68.2	20.0	-
+SFT	46.1	53.5	35.6	64.8	69.3	55.6	30.5	50.4
DeepSeek-3B-16B	37.7	18.5	39.4	48.3	-	65.9	13.5	-
+Chat	48.5	46.5	40.8	70.1	74.8	85.6	32.3	57.0
Qwen1.5-3B-14B	60.4	13.5	27.2	60.2	-	73.4	20.9	-
+Chat	58.9	55.5	21.3	59.7	83.9	85.6	36.2	57.3
OLMoE (This Model)	49.8	3.0	33.6	22.4	-	59.7	16.6	-
+SFT	51.4	40.5	38.0	51.6	69.2	84.1	43.3	54.0
+DPO	51.9	45.5	37.0	54.8	84.0	82.6	48.1	57.7

Bias, Risks, and Limitations

This adapted OLMo model is a research artifact. It is intended to benefit the research community interested in understanding the safety properties of LLMs and developers building safety tools for LLMs. For this reason, the model does not include a specific safety filter or safety training data. While the model refuses some requests, it is possible for the model to generate harmful and sensitive content from some user prompts. We recommend developers exercise caution and consider the risks of the applications of this technology. Furthermore, developers should consider implementing safeguards for biases, privacy, and other potential harms when appropriate. Finally, as with every LLM, OLMo may produce factual-sounding outputs that may not be true, so developers and users are encouraged to confirm such outputs before relying on them. All users of this model are responsible for how they use the model.

Citation

TODO