Model reference · open weights
Olmo-Hybrid-SFT is an open-weight language model from allenai. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | allenai |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 7.4B |
| Context | 32k tokens |
| Runs with | transformers |
| Based on | allenai/Olmo-Hybrid-7B |
| Released | 2026-02-19 |
| Popularity | 6k downloads / month |
| Licence | Open weights |
About
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.
Olmo Hybrid is supported in transformers 5.3.0 or higher:
pip install transformers>=5.3.0
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]
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.
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.
olmo@allenai.org. Press: press@allenai.org| Skill | Benc
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys olmo-hybrid-sft for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (olmo-hybrid-sft below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"olmo-hybrid-sft","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.