Instructions to use birgermoell/Qwen3.5-4B-EU-Tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use birgermoell/Qwen3.5-4B-EU-Tool with Transformers:

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

pipe = pipeline("text-generation", model="birgermoell/Qwen3.5-4B-EU-Tool")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("birgermoell/Qwen3.5-4B-EU-Tool")
model = AutoModelForCausalLM.from_pretrained("birgermoell/Qwen3.5-4B-EU-Tool")
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/Qwen3.5-4B-EU-Tool with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "birgermoell/Qwen3.5-4B-EU-Tool"
# 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/Qwen3.5-4B-EU-Tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/birgermoell/Qwen3.5-4B-EU-Tool

SGLang

How to use birgermoell/Qwen3.5-4B-EU-Tool 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/Qwen3.5-4B-EU-Tool" \
    --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/Qwen3.5-4B-EU-Tool",
		"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/Qwen3.5-4B-EU-Tool" \
        --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/Qwen3.5-4B-EU-Tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use birgermoell/Qwen3.5-4B-EU-Tool with Docker Model Runner:
```
docker model run hf.co/birgermoell/Qwen3.5-4B-EU-Tool
```

Qwen3.5-4B-EU-Tool

A European, tool-use-capable post-training of Qwen/Qwen3.5-4B. It folds function-calling / agentic data into the EU instruction mix, so the model calls tools in Qwen3.5's native <tool_call> format while keeping European multilingual instruction following. Defaults to fast no-think answers with a /think reasoning toggle. Part of OpenEuroLLM Task 4.6, trained on LUMI (AMD MI250X / ROCm).

Tool-calling evaluation (BFCL-style)

A vLLM-free, self-contained function-calling eval (scripts/eval_tool_calling.py): 300 held-out prompts (150 that require a call + 150 that should not be called), scored on argument-exact match, function-name match, well-formed output, and relevance (correct abstention):

Metric	base Qwen3.5-4B	this model (SFT)
format_valid	0.993	0.98
name_acc	0.993	0.98
args_exact	0.873	0.96
abstain / relevance	0.807	0.84

The base is already tool-capable (native support); this post-training's win is argument precision (+8.7 pts args_exact) while improving relevance (knowing when not to call).

A note on RL. A verifiable-reward GRPO stage was also trained, but its tool_call_exact reward had no abstention component, so it reward-hacked — driving relevance to 0.0 (it always calls). We therefore ship the SFT, not the GRPO. A fixed reward (crediting correct abstention, with no-call examples) is the follow-up.

Training data

Openly-documented European post-training data (no proprietary data). SFT mix (~590k), packed, bf16:

General EU instructions (EuroBlocks EU-multilingual + Tülu-3 English replay), no-think.
Reasoning traces (chain-of-thought, think-format) — keeps the /think path sharp.
Defect-repair (native text for weak EU languages), oversampled.
Tool-use (~10%) — birgermoell/oellm-eu-tooluse-v1: ~55k function-calling examples normalized to Qwen3.5-native format from Glaive-v2 / ToolACE / Hermes (all Apache-2.0).

Usage (with tools)

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

mid = "birgermoell/Qwen3.5-4B-EU-Tool"
tok = AutoTokenizer.from_pretrained(mid)
model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16, device_map="auto")

tools = [{"type": "function", "function": {
    "name": "get_weather",
    "description": "Get the current weather for a city",
    "parameters": {"type": "object",
        "properties": {"city": {"type": "string"}}, "required": ["city"]}}}]
msgs = [{"role": "user", "content": "Vad är vädret i Stockholm?"}]
ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True,
                             return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=200)[0, ids.shape[1]:], skip_special_tokens=True))
# -> <tool_call>\n{"name": "get_weather", "arguments": {"city": "Stockholm"}}\n</tool_call>

No-think (default): fast, direct answers / tool calls.
Thinking: add /think (or enable_thinking=True) for step-by-step reasoning.

License

Apache-2.0 (inherits the Qwen3.5-4B base license). Built within OpenEuroLLM.

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Safetensors

Model size

5B params

Tensor type

BF16

Model tree for birgermoell/Qwen3.5-4B-EU-Tool

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