README.md

---
license: apache-2.0
datasets:
- allenai/dolmino-mix-1124
- allenai/dolma
language:
- en
---

## Model Details

<img alt="OLMo Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmo2/olmo.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">


# Model Card for OLMo 2 7B

We introduce OLMo 2, a new family of 7B and 13B models featuring a 9-point increase in MMLU, among other evaluation improvements, compared to the original [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) model. These gains come from training on [OLMo-mix-1124](https://huggingface.co/datasets/allenai/olmo-mix-1124) and [Dolmino-mix-1124](https://huggingface.co/datasets/allenai/dolmino-mix-1124) datasets and staged training approach.

OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. 
These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. 

| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|------|--------|---------|-------------|-----------------|----------------|
| [OLMo 2-7B](https://huggingface.co/allenai/OLMo-2-1124-7B) | 4 Trillion   | 32     | 4096        | 32              |  4096  |
| [OLMo 2-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) | 5 Trillion   | 40     | 5120        | 42              |  4096  |

The core models released in this batch include the following:

| **Stage**           | **OLMo 2 7B**                                                                                          | **OLMo 2 13B**                                                                                         |
|----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| **Base Model**       | [allenai/OLMo-2-1124-7B](https://huggingface.co/allenai/OLMo-2-1124-7B)                                | [allenai/OLMo-2-1124-13B](https://huggingface.co/allenai/OLMo-2-1124-13B)                             |
| **SFT**              | [allenai/OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT)                | [allenai/OLMo-2-1124-13B-SFT](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT)              |
| **DPO**              | [allenai/OLMo-2-1124-7B-DPO](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO)                | [allenai/OLMo-2-1124-13B-DPO](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO)              |
| **Final Models (RLVR)** | [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct)                        | [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct)                      |
| **Reward Model (RM)**| [allenai/OLMo-2-1124-7B-RM](https://huggingface.co/allenai/OLMo-2-1124-7B-RM)                                                     | (Same as 7B)                                                     |


## Inference

You can use OLMo with the standard HuggingFace transformers library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-7B")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-1124-7B")
message = ["Language modeling is "]
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])
>> 'Language modeling is the first step to build natural language generation...'
```

For faster performance, you can quantize the model using the following method:
```python
AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-7B", 
    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:
```python
inputs.input_ids.to('cuda')
```

We have released checkpoints for these models, for every 1000 training steps.
The naming convention is `stepXXX-tokensYYYB`.

To load a specific model revision with HuggingFace, simply add the argument `revision`:
```bash
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-7B", revision="step1000-tokens5B")
```

Or, you can access all the revisions for the models via the following code snippet:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/OLMo-2-1124-7B")
branches = [b.name for b in out.branches]
```

### Fine-tuning
Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available.
1. Fine-tune with the OLMo repository:
```bash
torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
    --data.paths=[{path_to_data}/input_ids.npy] \
    --data.label_mask_paths=[{path_to_data}/label_mask.npy] \
    --load_path={path_to_checkpoint} \
    --reset_trainer_state
```
For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo?tab=readme-ov-file#fine-tuning).

2. Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are [here](https://github.com/allenai/open-instruct).

### Model Description

- **Developed by:** Allen Institute for AI (Ai2)
- **Model type:** a Transformer style autoregressive language model.
- **Language(s) (NLP):** English
- **License:** The code and model are released under Apache 2.0.
- **Contact:** Technical inquiries: `olmo@allenai.org`. Press: `press@allenai.org`
- **Date cutoff:** Dec. 2023.


### Model Sources

- **Project Page:** https://allenai.org/olmo
- **Repositories:** 
    - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
    - Evaluation code: https://github.com/allenai/OLMo-Eval
    - Further fine-tuning code: https://github.com/allenai/open-instruct
- **Paper:** Coming soon
<!-- - **Technical blog post:** https://blog.allenai.org/olmo-1-7-7b-a-24-point-improvement-on-mmlu-92b43f7d269d  -->
<!-- - **W&B Logs:** [pretraining](https://wandb.ai/ai2-llm/OLMo-7B/groups/OLMo-1.7-7B), [annealing](https://wandb.ai/ai2-llm/OLMo-7B/groups/OLMo-1.7-7B-anneal) -->


## Evaluation
Core model results for OLMo 2 7B and 13B models are found below.

| Model | Train FLOPs | Average | ARC/C | HSwag | WinoG | MMLU | DROP | NQ | AGIEval | GSM8k | MMLUPro | TriviaQA |
|-------------------|------------|---------|--------|--------|--------|-------|-------|-----|----------|--------|-----------|-----------|
| *Open weights models:* |
| Llama-2-13B | 1.6·10²³ | 54.1 | 67.3 | 83.9 | 74.9 | 55.7 | 45.6 | 38.4 | 41.5 | 28.1 | 23.9 | 81.3 |
| Mistral-7B-v0.3 | n/a | 58.8 | 78.3 | 83.1 | 77.7 | 63.5 | 51.8 | 37.2 | 47.3 | 40.1 | 30 | 79.3 |
| Llama-3.1-8B | 7.2·10²³ | 61.8 | 79.5 | 81.6 | 76.6 | 66.9 | 56.4 | 33.9 | 51.3 | 56.5 | 34.7 | 80.3 |
| Mistral-Nemo-12B | n/a | 66.9 | 85.2 | 85.6 | 81.5 | 69.5 | 69.2 | 39.7 | 54.7 | 62.1 | 36.7 | 84.6 |
| Qwen-2.5-7B | 8.2·10²³ | 67.4 | 89.5 | 89.7 | 74.2 | 74.4 | 55.8 | 29.9 | 63.7 | 81.5 | 45.8 | 69.4 |
| Gemma-2-9B | 4.4·10²³ | 67.8 | 89.5 | 87.3 | 78.8 | 70.6 | 63 | 38 | 57.3 | 70.1 | 42 | 81.8 |
| Qwen-2.5-14B | 16.0·10²³ | 72.2 | 94 | 94 | 80 | 79.3 | 51.5 | 37.3 | 71 | 83.4 | 52.8 | 79.1 |
| *Partially open models:* |
| StableLM-2-12B | 2.9·10²³ | 62.2 | 81.9 | 84.5 | 77.7 | 62.4 | 55.5 | 37.6 | 50.9 | 62 | 29.3 | 79.9 |
| Zamba-2-7B | n/c | 65.2 | 92.2 | 89.4 | 79.6 | 68.5 | 51.7 | 36.5 | 55.5 | 67.2 | 32.8 | 78.8 |
| *Fully open models:* |
| Amber-7B | 0.5·10²³ | 35.2 | 44.9 | 74.5 | 65.5 | 24.7 | 26.1 | 18.7 | 21.8 | 4.8 | 11.7 | 59.3 |
| OLMo-7B | 1.0·10²³ | 38.3 | 46.4 | 78.1 | 68.5 | 28.3 | 27.3 | 24.8 | 23.7 | 9.2 | 12.1 | 64.1 |
| MAP-Neo-7B | 2.1·10²³ | 49.6 | 78.4 | 72.8 | 69.2 | 58 | 39.4 | 28.9 | 45.8 | 12.5 | 25.9 | 65.1 |
| OLMo-0424-7B | 0.9·10²³ | 50.7 | 66.9 | 80.1 | 73.6 | 54.3 | 50 | 29.6 | 43.9 | 27.7 | 22.1 | 58.8 |
| DCLM-7B | 1.0·10²³ | 56.9 | 79.8 | 82.3 | 77.3 | 64.4 | 39.3 | 28.8 | 47.5 | 46.1 | 31.3 | 72.1 |
| **OLMo-2-1124-7B** | 1.8·10²³ | 62.9 | 79.8 | 83.8 | 77.2 | 63.7 | 60.8 | 36.9 | 50.4 | 67.5 | 31 | 78 |
| **OLMo-2-1124-13B** | 4.6·10²³ | 68.3 | 83.5 | 86.4 | 81.5 | 67.5 | 70.7 | 46.7 | 54.2 | 75.1 | 35.1 | 81.9 |

## Model Details

### Pretraining
|  | **OLMo 2 7B** | **OLMo 2 13B** |
|-------------------|------------|------------|
| Pretraining Stage 1<br>([OLMo-Mix-1124](https://huggingface.co/datasets/allenai/olmo-mix-1124)) | 4 trillion tokens<br>(1 epoch) | 5 trillion tokens<br>(1.2 epochs) |
| Pretraining Stage 2<br>([Dolmino-Mix-1124](https://huggingface.co/datasets/allenai/dolmino-mix-1124)) | 50B tokens (3 runs)<br>*merged* | 100B tokens (3 runs)<br>300B tokens (1 run)<br>*merged* |
| Post-training<br>([Tulu 3 SFT OLMo mix](https://huggingface.co/datasets/allenai/tulu-3-sft-olmo-mixture)) | SFT + DPO + PPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix)) | SFT + DPO + PPO<br>([preference mix](https://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mix)) |

#### Stage 1: Initial Pretraining
- Dataset: [OLMo-Mix-1124](https://huggingface.co/datasets/allenai/olmo-mix-1124) (3.9T tokens)
- Coverage: 90%+ of total pretraining budget
- 7B Model: ~1 epoch
- 13B Model: 1.2 epochs (5T tokens)

#### Stage 2: Fine-tuning
- Dataset: [Dolmino-Mix-1124](https://huggingface.co/datasets/allenai/dolmino-mix-1124) (843B tokens)
- Three training mixes:
  - 50B tokens
  - 100B tokens
  - 300B tokens
- Mix composition: 50% high-quality data + academic/Q&A/instruction/math content

#### Model Merging
- 7B Model: 3 versions trained on 50B mix, merged via model souping
- 13B Model: 3 versions on 100B mix + 1 version on 300B mix, merged for final checkpoint


## 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.


## Citation
A technical manuscript is forthcoming!

## Model Card Contact
For errors in this model card, contact `olmo@allenai.org`.