Olmo-Hybrid-SFT

Model Details

Model Card for Olmo Hybrid Instruct SFT

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:

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/Olmo-Hybrid-Instruct-SFT-7B")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-Hybrid-Instruct-SFT-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])
>> '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/Olmo-Hybrid-Instruct-SFT-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/Olmo-Hybrid-Instruct-SFT-7B", revision="step3000")

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/Olmo-Hybrid-Instruct-SFT-7B")
branches = [b.name for b in out.branches]

Chat template

Default System Message

The default system prompt for this model is:

You are a helpful function-calling AI assistant.
You do not currently have access to any functions.

Chat Format

The chat template for this model is formatted as:

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

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: olmo@allenai.org. Press: press@allenai.org
• 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 | Benc