Instructions to use birgermoell/Qwen3.5-2B-EU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use birgermoell/Qwen3.5-2B-EU with Transformers:

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

pipe = pipeline("text-generation", model="birgermoell/Qwen3.5-2B-EU")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("birgermoell/Qwen3.5-2B-EU")
model = AutoModelForCausalLM.from_pretrained("birgermoell/Qwen3.5-2B-EU")
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 birgermoell/Qwen3.5-2B-EU with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "birgermoell/Qwen3.5-2B-EU"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "birgermoell/Qwen3.5-2B-EU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/birgermoell/Qwen3.5-2B-EU

SGLang

How to use birgermoell/Qwen3.5-2B-EU 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 "birgermoell/Qwen3.5-2B-EU" \
    --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": "birgermoell/Qwen3.5-2B-EU",
		"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 "birgermoell/Qwen3.5-2B-EU" \
        --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": "birgermoell/Qwen3.5-2B-EU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use birgermoell/Qwen3.5-2B-EU with Docker Model Runner:
```
docker model run hf.co/birgermoell/Qwen3.5-2B-EU
```

Qwen3.5-2B-EU

A compact European post-training of Qwen/Qwen3.5-2B, specialised for the OpenEuroLLM target languages. Small enough for laptops / future on-device use, and it defaults to fast no-think answers while keeping the /think reasoning toggle.

Part of a mobile-oriented lineup (2B + 4B). Built with a reproducible SFT → SimPO pipeline on European multilingual data.

What we changed and why it's better

Post-training lifts the base model substantially on the OpenEuroLLM EU eval holdouts (38 European languages × 10 task buckets, deterministic scoring, full dev split = 1368 rows):

Model	Overall accuracy
Qwen3.5-2B (base)	32.7%
Qwen3.5-2B-EU	45.6% (+12.9)

Gains are broad across languages and tasks — e.g. grounded QA 43→95, civic/safety 12→90, reasoning-math 32→61; per-language examples: uk 31→53, sk 33→58, is 31→56, nl 22→47, eu 25→47. (Per-cell language/bucket numbers are small-sample; the +12.9 overall is the robust signal.)

Training data

All stages use openly-documented European post-training data (OpenEuroLLM Task 4.6); no proprietary data. Two stages: SFT → SimPO (this 2B keeps the lighter "v1" recipe — at 2B scale, longer training + a GRPO stage overfit and did not help).

1. Supervised fine-tuning (SFT) — ~400k examples, packed, bf16:

General EU instructions (~85%, ~340k) — Dolci tulu3-euroblocks-85-15: EuroBlocks EU-multilingual instruction data (85%) + Tülu-3 (allenai/tulu-3) English replay (15%), adding EU-language instruction-following while preserving English. Formatted no-think (direct answers) so the model defaults to fast replies.
Reasoning traces (~15%, ~60k) — chain-of-thought SFT (Dolci-Think / OpenThoughts / OpenMathInstruct-family), think-format, so the /think path stays sharp. (Currently English+Finnish reasoning — the known multilingual-reasoning gap; EU-language reasoning distillation is in progress.)

2. Preference optimisation (SimPO, reference-free) — preference accuracy ≈ 0.79:

birgermoell/oellm-eu-exam-mcq-v1 (preference/DPO track) — European exam multiple-choice preference pairs (correct-over-incorrect) across ~35 languages, 28 sources (national, medical/licensing, and academic exams), mixed licenses (filterable per row).

Evaluation — held out from training: birgermoell/oellm-eu-eval-holdouts-v1 — 38 EU languages × 10 task buckets, deterministic per-task scoring.

Swedish capability (open-ended rubric)

A vLLM-free Swedish eval (Qwen3.5 isn't yet supported by EuroEval's fast backends): 24 prompts across 9 categories (sentiment, Swedish knowledge, reasoning, summarization, linguistic correctness, instruction-following, common sense, creative), scored 1–5 on språkkvalitet / korrekthet / instruktion / hjälpsamhet, plus automatic langdetect Swedish-purity.

Dimension	2B-EU	(4B-EU)	(9B-EU)
Swedish-purity (langdetect)	1.00	1.00	1.00
Språkkvalitet (fluency/grammar)	4.3	4.6	4.7
Korrekthet (factual/logical)	2.7	4.4	4.6
Instruktion (follows constraints)	3.4	4.0	4.5
Hjälpsamhet	2.9	4.3	4.5

The 2B writes fully in-Swedish, fluent prose (purity 1.00) and is good at summarization and grammar, but is factually unreliable at 2B scale — it hallucinates Swedish facts (e.g. called Göta älv a lake; said the sky is blue "because of clouds"). For accuracy, prefer the 4B-EU / 9B-EU. Tooling: scripts/eval_swedish_rubric.py + data/swedish_rubric_prompts.jsonl.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer  # transformers >= 5.5 (qwen3_5 arch)
import torch

mid = "birgermoell/Qwen3.5-2B-EU"
tok = AutoTokenizer.from_pretrained(mid)
model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "Vad är meningen med livet? Svara kort."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=200)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

No-think (default): fast, direct answers — best for interactive/mobile use.
Thinking: add /think to the user turn (or enable_thinking=True in the chat template) for step-by-step reasoning on harder tasks.

On-device status

bf16 (this repo): runs via transformers / vLLM. Recommended today.
GGUF (llama.cpp / Ollama): ⚠️ not supported yet — llama.cpp drops Qwen3.5's hybrid linear-attention (Gated Delta Net) layers during conversion (load fails with a missing tensor). Blocked upstream.
MLX (Apple): ⚠️ not supported yet — mlx-lm raises Model type qwen3_5 not supported.

Qwen3.5's hybrid architecture is very new (Mar 2026), so the on-device quantization runtimes have not yet added support. True phone deployment will work once llama.cpp / MLX add Gated-Delta-Net support; until then use the bf16 weights via transformers/vLLM.

Limitations

2B scale: best for assistant/chat in EU languages, not a frontier reasoner. Some buckets (tool-calling, locale-formatting, long summarization) remain weak at this size. Inherits the base model's general knowledge and any biases.

License

Apache-2.0 (inherits the Qwen3.5-2B base license). Built within OpenEuroLLM.

Downloads last month: 104

Safetensors

Model size

2B params

Tensor type

F32

Model tree for birgermoell/Qwen3.5-2B-EU

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

(216)

this model