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unsloth/Olmo-3-7B-Instruct-GGUF

sourceHugging Faceapache-2.0updated 10mo agoView on Hugging Face

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Model Details

Model Card for Olmo 3 7B Instruct

We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.

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.

The core models released in this batch include the following:

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

Installation

Olmo 3 is supported in transformers 4.57.0 or higher:

bash

pip install transformers>=4.57.0

Inference

You can use OLMo with the standard HuggingFace transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Instruct")
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])
>> 'This is a fun and imaginative question! Let’s break it down...'

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

python

AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct", 
    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 post-training, the naming convention is step_XXXX.

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

bash

olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct", revision="step_300")

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-3-7B-Instruct")
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_end|>
<|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: 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
—Paper: [TBD]

Evaluation

Skill	Benchmark	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	65.1	79.6	87.3	82.3	91.6	71.0	30.1	21.9	67.3
	AIME 2024	6.7	23.5	44.3	26.2	55.1	11.3	1.3	0.5	7.3
	AIME 2025	7.2	20.4	32.5	21.7	43.3	6.3	0.4	0.2	6.3
	OMEGA	14.4	22.8	28.9	20.5	32.3	13.7	5.2	5.0	10.7
Reasoning	BigBenchHard	51.0	69.3	71.2	73.7	85.6	68.8	43.8	42.2	61.2
	ZebraLogic	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	69.8	72.9	77.2	79.8	82.9	74.9	25.8	34.4	64.0
	MBPP+	56.5	55.9	60.2	64.4	66.3	62.6	40.7	42.1	54.0
	LiveCodeBench v3	20.0	18.8	29.5	53.2	55.9	34.5	7.2	7.8	11.5
IF	IFEval	81.7	82.0	85.6	86.3	87.8	73.4	72.2	71.4	77.5
	IFBench	27.4	29.3	32.3	29.3	34.0	28.4	26.7	22.1	22.3
Knowledge	MMLU	67.1	69.1	69.1	80.4	83.6	77.2	61.6	62.7	63.5
QA	PopQA	16.5	20.7	14.1	20.4	26.5	21.5	25.5	25.5	28.9
	GPQA	30.0	37.9	40.4	44.6	51.1	35.6	31.3	28.8	33.0
Chat	AlpacaEval 2 LC	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-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
—Datasets: Dolci-Think-SFT-7B, Dolci-Instruct-SFT-7B

Stage 2:DPO

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

Stage 3: RLVR

—reinforcement learning from verifiable rewards on the Dolci-Think-RL-7B dataset. This dataset consits of math, code, instruction-following, and general chat queries.
—Datasets: Dolci-Think-RL-7B, Dolci-Instruct-RL-7B

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

A technical manuscript is forthcoming!

Model Card Contact

For errors in this model card, contact olmo@allenai.org.