allenai/Olmo-Hybrid-Instruct-DPO-7B

Model Details

Model Card for Olmo Hybrid Instruct DPO

We expand on our Olmo model series by introducing Olmo Hybrid, a new 7B hybrid RNN model in the Olmo family. Olmo Hybrid dramatically outperforms Olmo 3 in final performance, consistently showing roughly 2x data efficiency on core evals over the course of our pretraining run. We also show gains in performance on long-context benchmarks, as well as improved inference efficiency (throughput and memory) on long-context lengths by a factor of 75%.

The core models released in this batch include the following:

Stage	Olmo 3 7B Think	Olmo 3 32B Think	Olmo 3 7B Instruct	Olmo Hybrid Think 7B	Olmo Hybrid Instruct 7B
Base Model	Olmo-3-7B	Olmo-3-32B	Olmo-3-7B	Olmo-Hybrid-7B	Olmo-Hybrid-7B
SFT	Olmo-3-7B-Think-SFT	Olmo-3-32B-Think-SFT	Olmo-3-7B-Instruct-SFT	Olmo-Hybrid-Think-SFT-7B	Olmo-Hybrid-Instruct-SFT-7B
DPO	Olmo-3-7B-Think-DPO	Olmo-3-32B-Think-DPO	Olmo-3-7B-Instruct-DPO	--	Olmo-Hybrid-Instruct-DPO-7B
Final Models (RLVR)	Olmo-3-7B-Think	Olmo-3-32B-Think	Olmo-3-7B-Instruct	--	--

Olmo is a series of Open language models designed to enable the science of language models. These models are pre-trained on the Dolma 3 dataset and post-trained on the Dolci datasets. We are releasing all code, checkpoints, logs (coming soon), and associated training details.

Installation

Olmo Hybrid is supported in transformers 5.3.0 or higher:

pip install transformers>=5.3.0

Inference

You can use OLMo with the standard HuggingFace transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/allenai/Olmo-Hybrid-Instruct-DPO-7B")
tokenizer = AutoTokenizer.from_pretrained("allenai/allenai/Olmo-Hybrid-Instruct-DPO-7B")
message = ["Who would win in a fight - a dinosaur or a cow named Moo Moo?"]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> '<think>Okay, so the question is who would win in a fight...'

For faster performance, you can quantize the model using the following method:

AutoModelForCausalLM.from_pretrained("allenai/allenai/Olmo-Hybrid-Instruct-DPO-7B", 
    torch_dtype=torch.float16, 
    load_in_8bit=True)  # Requires bitsandbytes

The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:

inputs.input_ids.to('cuda')

We have released checkpoints for these models. For post-training, the naming convention is step_XXXX.

To load a specific model revision with HuggingFace, simply add the argument revision:

olmo = AutoModelForCausalLM.from_pretrained("allenai/allenai/Olmo-Hybrid-Instruct-DPO-7B", revision="main")

Or, you can access all the revisions for the models via the following code snippet:

from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/allenai/Olmo-Hybrid-Instruct-DPO-7B")
branches = [b.name for b in out.branches]

Chat template

Default System Message

The default system prompt for this model is:

<|im_start|>system
You are a helpful function-calling AI assistant. You do not currently have access to any functions. <functions></functions>
<|im_end|>

Chat Format

The chat template for this model is formatted as:

<|im_start|>system
You are a helpful function-calling AI assistant. You do not currently have access to any functions. <functions></functions>
<|im_start|>user
Who would win in a fight - a dinosaur or a cow named Moo Moo?<|im_end|>
<|im_start|>assistant
This is a fun and imaginative question! Let’s break it down...
Moo Moo the cow would certinaly win.
<|endoftext|>

Model Description

• Developed by: Allen Institute for AI (Ai2)
• Model type: a Transformer style autoregressive language model.
• Language(s) (NLP): English
• License: This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
• Contact: Technical inquiries: [email protected]. Press: [email protected]
• Date cutoff: Dec. 2024.

Model Sources

• Project Page: https://allenai.org/olmo
• Repositories:
  • Open-Instruct for DPO and RLVR: https://github.com/allenai/open-instruct
  • OLMo-Core for pre-training and SFT: https://github.com/allenai/OLMo-core
  • OLMo-Eval for evaluation: https://github.com/allenai/OLMo-Eval
• Olmo 3 Paper: https://allenai.org/papers/olmo3
• Olmo Hybrid Paper: https://allenai.org/papers/olmo-hybrid

Evaluation

Skill	Benchmark	Olmo Hybrid Instruct SFT 7B	Olmo Hybrid Instruct DPO 7B	Olmo 3 Instruct 7B SFT	Olmo 3 Instruct 7B DPO	Olmo3 Instruct 7B	Qwen 3 8B (no reasoning)	Qwen 3 VL 8B Instruct	Qwen 2.5 7B	Olmo 2 7B Instruct	Apertus 8B Instruct	Granite 3.3 8B Instruct
Math	MATH	66.7	72.9	65.1	79.6	87.3	82.3	91.6	71.0	30.1	21.9	67.3
	AIME 2024	6.7	10.1	6.7	23.5	44.3	26.2	55.1	11.3	1.3	0.5	7.3
	AIME 2025	8.8	10.2	7.2	20.4	32.5	21.7	43.3	6.3	0.4	0.2	6.3
	OMEGA	16.0	19.5	14.4	22.8	28.9	20.5	32.3	13.7	5.2	5.0	10.7
Reasoning	BigBenchHard	47.3	57.3	51.0	69.3	71.2	73.7	85.6	68.8	43.8	42.2	61.2
	ZebraLogic	17.0	29.1	18.0	28.4	32.9	25.4	64.3	10.7	5.3	5.3	17.6
	AGI Eval English			59.2	64.0	64.4	76.0	84.5	69.8	56.1	50.8	64.0
Coding	HumanEvalPlus	69.2	75.1	69.8	72.9	77.2	79.8	82.9	74.9	25.8	34.4	64.0
	MBPP+	55.3	56.9	56.5	55.9	60.2	64.4	66.3	62.6	40.7	42.1	54.0
	LiveCodeBench v3	21.3	22.0	20.0	18.8	29.5	53.2	55.9	34.5	7.2	7.8	11.5
IF	IFEval	81.5	80.5	81.7	82.0	85.6	86.3	87.8	73.4	72.2	71.4	77.5
	IFBench	29.0	33.3	27.4	29.3	32.3	29.3	34.0	28.4	26.7	22.1	22.3
Knowledge	MMLU	71.9	73.6	67.1	69.1	69.1	80.4	83.6	77.2	61.6	62.7	63.5
QA	PopQA	16.8	21.0	16.5	20.7	14.1	20.4	26.5	21.5	25.5	25.5	28.9
	GPQA	36.8	38.0	30.0	37.9	40.4	44.6	51.1	35.6	31.3	28.8	33.0
Chat	AlpacaEval 2 LC	25.6	56.3	21.8	43.3	40.9	49.8	73.5	23.0	18.3	8.1	28.6
Tool Use	SimpleQA			74.2	79.8	79.3	79.0	90.3	78.0	–	–	–
	LitQA2			38.0	43.3	38.2	39.6	30.7	29.8	–	–	–
	BFCL			48.9	49.6	49.8	60.2	66.2	55.8	–	–	–
Safety	Safety			89.2	90.2	87.3	78.0	80.2	73.4	93.1	72.2	73.7

Model Details

Stage 1: SFT

• supervised fine-tuning on the Dolci-Instruct-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
• Datasets: Dolci-Instruct-SFT-7B

Stage 2:DPO

• direct preference optimization on the Dolci-Instruct-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
• Datasets: Dolci-Instruct-DPO-7B

Inference & Recommended Settings

We evaluated our models on the following settings. We also recommend using them for generation:

• temperature: 0.6
• top_p: 0.95
• max_tokens: 32768

transformers Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/Olmo-Hybrid-Instruct-DPO-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    temperature=0.6,
    top_p=0.95,
    max_new_tokens=32768,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vllm Example

from vllm import LLM, SamplingParams

model_id = "allenai/Olmo-Hybrid-Instruct-DPO-7B"
llm = LLM(
    model=model_id,
    mamba_ssm_cache_dtype="float32",
)

sampling_params = SamplingParams(
    temperature=0.6,
    top_p=0.95,
    max_tokens=32768,
)

prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
outputs = llm.generate(prompt, sampling_params)
print(outputs[0].outputs[0].text)

Bias, Risks, and Limitations

Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Citation

Coming Soon!

Model Card Contact

For errors in this model card, contact [email protected].