OLMo-2-0425-1B GGUF Models
Model Generation Details
This model was generated using llama.cpp at commit 8c83449.
Choosing the Right Model Format
Selecting the correct model format depends on your hardware capabilities and memory constraints.
BF16 (Brain Float 16) – Use if BF16 acceleration is available
- A 16-bit floating-point format designed for faster computation while retaining good precision.
- Provides similar dynamic range as FP32 but with lower memory usage.
- Recommended if your hardware supports BF16 acceleration (check your device's specs).
- Ideal for high-performance inference with reduced memory footprint compared to FP32.
📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.
📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
F16 (Float 16) – More widely supported than BF16
- A 16-bit floating-point high precision but with less of range of values than BF16.
- Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.
📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.
Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
- Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.
📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.
📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)
These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.
IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
- Use case: Best for ultra-low-memory devices where even Q4_K is too large.
- Trade-off: Lower accuracy compared to higher-bit quantizations.
IQ3_S: Small block size for maximum memory efficiency.
- Use case: Best for low-memory devices where IQ3_XS is too aggressive.
IQ3_M: Medium block size for better accuracy than IQ3_S.
- Use case: Suitable for low-memory devices where IQ3_S is too limiting.
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
- Use case: Best for low-memory devices where Q6_K is too large.
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
- Use case: Best for ARM-based devices or low-memory environments.
Summary Table: Model Format Selection
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|---|---|---|---|---|
| BF16 | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
| F16 | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
| Q4_K | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
| Q6_K | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
| Q8_0 | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
| IQ3_XS | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
| Q4_0 | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
Included Files & Details
OLMo-2-0425-1B-bf16.gguf
- Model weights preserved in BF16.
- Use this if you want to requantize the model into a different format.
- Best if your device supports BF16 acceleration.
OLMo-2-0425-1B-f16.gguf
- Model weights stored in F16.
- Use if your device supports FP16, especially if BF16 is not available.
OLMo-2-0425-1B-bf16-q8_0.gguf
- Output & embeddings remain in BF16.
- All other layers quantized to Q8_0.
- Use if your device supports BF16 and you want a quantized version.
OLMo-2-0425-1B-f16-q8_0.gguf
- Output & embeddings remain in F16.
- All other layers quantized to Q8_0.
OLMo-2-0425-1B-q4_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q4_K.
- Good for CPU inference with limited memory.
OLMo-2-0425-1B-q4_k_s.gguf
- Smallest Q4_K variant, using less memory at the cost of accuracy.
- Best for very low-memory setups.
OLMo-2-0425-1B-q6_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q6_K .
OLMo-2-0425-1B-q8_0.gguf
- Fully Q8 quantized model for better accuracy.
- Requires more memory but offers higher precision.
OLMo-2-0425-1B-iq3_xs.gguf
- IQ3_XS quantization, optimized for extreme memory efficiency.
- Best for ultra-low-memory devices.
OLMo-2-0425-1B-iq3_m.gguf
- IQ3_M quantization, offering a medium block size for better accuracy.
- Suitable for low-memory devices.
OLMo-2-0425-1B-q4_0.gguf
- Pure Q4_0 quantization, optimized for ARM devices.
- Best for low-memory environments.
- Prefer IQ4_NL for better accuracy.
🚀 If you find these models useful
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Choose an AI assistant type:
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Model Details
Model Card for OLMo 2 1B
We introduce OLMo 2 1B, the smallest model in the OLMo 2 family. OLMo 2 was pre-trained on OLMo-mix-1124 and uses Dolmino-mix-1124 for mid-training.
OLMo 2 is the latest in a series of Open Language Models designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on GitHub.
| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|---|---|---|---|---|---|
| OLMo 2-1B | 4 Trillion | 16 | 2048 | 16 | 4096 |
| OLMo 2-7B | 4 Trillion | 32 | 4096 | 32 | 4096 |
| OLMo 2-13B | 5 Trillion | 40 | 5120 | 40 | 4096 |
| OLMo 2-32B | 6 Trillion | 64 | 5120 | 40 | 4096 |
The core models released in this batch include the following:
| Stage | OLMo 2 1B | OLMo 2 7B | OLMo 2 13B | OLMo 2 32B |
|---|---|---|---|---|
| Base Model | allenai/OLMo-2-0425-1B | allenai/OLMo-2-1124-7B | allenai/OLMo-2-1124-13B | allenai/OLMo-2-0325-32B |
| SFT | allenai/OLMo-2-0425-1B-SFT | allenai/OLMo-2-1124-7B-SFT | allenai/OLMo-2-1124-13B-SFT | allenai/OLMo-2-0325-32B-SFT |
| DPO | allenai/OLMo-2-0425-1B-DPO | allenai/OLMo-2-1124-7B-DPO | allenai/OLMo-2-1124-13B-DPO | allenai/OLMo-2-0325-32B-DPO |
| Final Models (RLVR) | allenai/OLMo-2-0425-1B-Instruct | allenai/OLMo-2-1124-7B-Instruct | allenai/OLMo-2-1124-13B-Instruct | allenai/OLMo-2-0325-32B-Instruct |
| Reward Model (RM) | allenai/OLMo-2-1124-7B-RM | (Same as 7B) |
Installation
OLMo 2 1B is supported in transformers v4.48 or higher:
pip install transformers>=4.48
If using vLLM, you will need to install from the main branch until v0.7.4 is released. Please
Inference
You can use OLMo with the standard HuggingFace transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B")
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 a key component of any text-based application, but its effectiveness...'
For faster performance, you can quantize the model using the following method:
AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B",
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 pretraining, the naming convention is stage1-stepXXX-tokensYYYB. For checkpoints with ingredients of the soup, the naming convention is stage2-ingredientN-stepXXX-tokensYYYB
To load a specific model revision with HuggingFace, simply add the argument revision:
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")
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-2-0425-1B")
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.
- Fine-tune with the OLMo repository:
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.
- Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are here.
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:
[email protected]. Press:[email protected] - 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: https://arxiv.org/abs/2501.00656
Evaluation
Core model results for OLMo 2 1B are found below.
| Instruct Model | Avg | FLOP×10²³ | AE2 | BBH | DROP | GSM8K | IFE | MATH | MMLU | Safety | PQA | TQA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Closed API models | ||||||||||||
| GPT-3.5 Turbo 0125 | 60.5 | n/a | 38.7 | 66.6 | 70.2 | 74.3 | 66.9 | 41.2 | 70.2 | 69.1 | 45.0 | 62.9 |
| GPT 4o Mini 0724 | 65.7 | n/a | 49.7 | 65.9 | 36.3 | 83.0 | 83.5 | 67.9 | 82.2 | 84.9 | 39.0 | 64.8 |
| Open weights models 1-1.7B Parameters | ||||||||||||
| SmolLM2 1.7B | 34.2 | 1.1 | 5.8 | 39.8 | 30.9 | 45.3 | 51.6 | 20.3 | 34.3 | 52.4 | 16.4 | 45.3 |
| Gemma 3 1B | 38.3 | 1.2 | 20.4 | 39.4 | 25.1 | 35.0 | 60.6 | 40.3 | 38.9 | 70.2 | 9.6 | 43.8 |
| Llama 3.1 1B | 39.3 | 6.7 | 10.1 | 40.2 | 32.2 | 45.4 | 54.0 | 21.6 | 46.7 | 87.2 | 13.8 | 41.5 |
| Qwen 2.5 1.5B | 41.7 | 1.7 | 7.4 | 45.8 | 13.4 | 66.2 | 44.2 | 40.6 | 59.7 | 77.6 | 15.5 | 46.5 |
| Fully-open models | ||||||||||||
| OLMo 1B 0724 | 24.4 | 0.22 | 2.4 | 29.9 | 27.9 | 10.8 | 25.3 | 2.2 | 36.6 | 52.0 | 12.1 | 44.3 |
| OLMo 2 1B | 42.7 | 0.35 | 9.1 | 35.0 | 34.6 | 68.3 | 70.1 | 20.7 | 40.0 | 87.6 | 12.9 | 48.7 |
Model Details
Training
| OLMo 2 1B | OLMo 2 7B | OLMo 2 13B | OLMo 2 32B | |
|---|---|---|---|---|
| Pretraining Stage 1 | 4 trillion tokens (1 epoch) |
4 trillion tokens (1 epoch) |
5 trillion tokens (1.2 epochs) |
6 trillion tokens (1.5 epochs) |
| Pretraining Stage 2 | 50B tokens | 50B tokens (3 runs) merged |
100B tokens (3 runs) 300B tokens (1 run) merged |
100B tokens (3 runs) 300B tokens (1 run) merged |
| Post-training | SFT+DPO+GRPO (preference mix) |
SFT + DPO + PPO (preference mix) |
SFT + DPO + PPO (preference mix) |
SFT + DPO + GRPO (preference mix) |
Stage 1: Initial Pretraining
- Dataset: OLMo-mix-1124 (3.9T tokens)
- Coverage: 95%+ of total pretraining budget
- 1B Model: ~1 epoch
Stage 2: Mid-training
- Dataset: Dolmino-Mix-1124
- One training mix:
- 50B tokens
- Mix composition: 50% high-quality web data + academic/Q&A/instruction/math content
Model Merging
- 1B Model: only 1 version is trained on a 50B mix, we did not merge.
Bias, Risks, and Limitations
Like any base or fine-tuned language model, AI can 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
@misc{olmo20242olmo2furious,
title={{2 OLMo 2 Furious}},
author={Team OLMo and Pete Walsh and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Shane Arora and Akshita Bhagia and Yuling Gu and Shengyi Huang and Matt Jordan and Nathan Lambert and Dustin Schwenk and Oyvind Tafjord and Taira Anderson and David Atkinson and Faeze Brahman and Christopher Clark and Pradeep Dasigi and Nouha Dziri and Michal Guerquin and Hamish Ivison and Pang Wei Koh and Jiacheng Liu and Saumya Malik and William Merrill and Lester James V. Miranda and Jacob Morrison and Tyler Murray and Crystal Nam and Valentina Pyatkin and Aman Rangapur and Michael Schmitz and Sam Skjonsberg and David Wadden and Christopher Wilhelm and Michael Wilson and Luke Zettlemoyer and Ali Farhadi and Noah A. Smith and Hannaneh Hajishirzi},
year={2024},
eprint={2501.00656},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.00656},
}
Model Card Contact
For errors in this model card, contact [email protected].
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