Instructions to use openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN")
model = AutoModelForCausalLM.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN

SGLang

How to use openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN with Docker Model Runner:
```
docker model run hf.co/openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN
```

OLMo-3-7B Dolci-Translated A-25EN (matched compute)

Continued-SFT of openeurollm/OLMo-3-7B-Instruct-SFT on a $25/75$ English:EU mixture, the EU-leaning configuration of the paper Translate, Replay, Mix: Exploring Multilingual Post-Training for Low-Resource European Languages. This is the matched-compute variant: both A-75EN and A-25EN consume the same $2.87$M samples × $2$ epochs at the same $1$M-token batch size; A-25EN takes 5398 steps to A-75EN's 3998 because the gemma-translated EU text packs more tokens per sample.

Qualitative completions viewer (this checkpoint's progression is on the right): https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/

Recipe


Base checkpoint	`openeurollm/OLMo-3-7B-Instruct-SFT` (our reproduction at parity with `allenai/OLMo-3-7B-Instruct-SFT`)
English half (Dolci replay)	`allenai/Dolci-Instruct-SFT`, 25% of the mixture
EU half (Dolci-Translated)	`openeurollm/Dolci-Instruct-SFT-translated`, 75% of the mixture, 7 EU languages translated with `gemma-3-27b-it`
EU languages	cs, de, es, fi, fr, it, sv
Total samples	4.62M (1,155,000 en + 3,465,000 EU)
Final step	5398 (matched-compute variant)
Chat template	`olmo` (inherited from base)

Training configuration

Identical to openeurollm/OLMo-3-7B-Dolci-Translated-A-75EN: AdamW, peak LR $8\times10^{-5}$, batch ${\sim}1$M tokens, seq_len 32k, BF16, DeepSpeed ZeRO 2, $8\times$H200 SXM. Only the English:EU ratio differs.

Evaluation

Bradley-Terry Elo (Qwen3.5-27B judge, 500 battles/language, 100 bootstraps):

Metric	A-25EN (this checkpoint)	Baseline (`openeurollm/OLMo-3-7B-Instruct-SFT`)
Overall Elo	$782 \pm 6$	$762 \pm 7$
English Elo	$913 \pm 16$	$954 \pm 16$
Non-English Elo	$\mathbf{742 \pm 8}$	$697 \pm 9$

Per-language Elo (cs / de / es / fi / fr / it / sv):

en	cs	de	es	fi	fr	it	sv
$913 \pm 16$	$\mathbf{745 \pm 15}$	$\mathbf{701 \pm 23}$	$\mathbf{763 \pm 18}$	$\mathbf{813 \pm 33}$	$\mathbf{756 \pm 17}$	$801 \pm 15$	$756 \pm 33$

A-25EN dominates the EU columns (best on cs, de, es, fi, fr) at the cost of $41$ English Elo points. Pick this checkpoint when the deployment is EU-facing and a moderate English regression is acceptable.

Intermediate checkpoints

Training-step revisions (step500, step1500, step2500, step3500) are available as HF git revisions of this repo (loaded via revision="step1500") and back the qualitative completions viewer at https://ferreirafabio.github.io/olmo3-multilingual-dolci-sft-progression/.

How to load

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN")
model = AutoModelForCausalLM.from_pretrained("openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN", torch_dtype="bfloat16")

Citation

Please cite the paper and the OLMo-3 family if you use this checkpoint.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN

Base model

allenai/Olmo-3-1025-7B

Finetuned

allenai/Olmo-3-7B-Instruct-SFT

Finetuned

openeurollm/OLMo-3-7B-Instruct-SFT

Finetuned

(2)

this model

openeurollm
/

OLMo-3-7B-Dolci-Translated-A-25EN

OLMo-3-7B Dolci-Translated A-25EN (matched compute)

Recipe

Training configuration

Evaluation

Intermediate checkpoints

How to load

Citation

Model tree for openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN

Datasets used to train openeurollm/OLMo-3-7B-Dolci-Translated-A-25EN