Instructions to use Boldt/Boldt-1B-IT-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Boldt/Boldt-1B-IT-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Boldt/Boldt-1B-IT-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Boldt/Boldt-1B-IT-Preview")
model = AutoModelForCausalLM.from_pretrained("Boldt/Boldt-1B-IT-Preview")
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

vLLM

How to use Boldt/Boldt-1B-IT-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Boldt/Boldt-1B-IT-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Boldt/Boldt-1B-IT-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Boldt/Boldt-1B-IT-Preview

SGLang

How to use Boldt/Boldt-1B-IT-Preview 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 "Boldt/Boldt-1B-IT-Preview" \
    --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": "Boldt/Boldt-1B-IT-Preview",
		"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 "Boldt/Boldt-1B-IT-Preview" \
        --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": "Boldt/Boldt-1B-IT-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Boldt/Boldt-1B-IT-Preview with Docker Model Runner:
```
docker model run hf.co/Boldt/Boldt-1B-IT-Preview
```

Boldt-1B-IT-Preview / README.md

alanakbik

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metadata

license: apache-2.0
language:
  - de
pipeline_tag: text-generation
library_name: transformers
tags:
  - instruction-tuned
  - german
base_model:
  - Boldt/Boldt-1B

Boldt-1B-IT-Preview

Boldt-1B-IT-Preview is a preview of an instruction-tuned German language model, fine-tuned on top of Boldt-1B. It is part of the Boldt series of German Small Language Models (SLMs) trained from scratch at Humboldt-Universität zu Berlin.

Boldt-DC-350M
Boldt-DC-1B
Boldt-1B
Boldt-1B-IT-Preview (this model)

Preview status. This is an early release intended to demonstrate instruction-following capabilities emerging from our quality-focused pre-training recipe. It has not undergone systematic safety evaluation and should not be used in production settings without further assessment.

Training data

Boldt-1B-IT-Preview was fine-tuned on a curated mixture of 95k German instruction-output pairs from four sources:

Aya: German subset of the Aya dataset, consisting of approximately 200 human-authored instruction-output pairs.
SmolTalk2 (DE, improved): an improved German subset of the SmolTalk2 dataset. We adjusted 52k prompts for more natural flowing German and regenerated outputs using Qwen-3.6-27B to improve their quality.
r/FragReddit: 7k prompts sourced from the r/FragReddit subreddit. Outputs were generated using Qwen-3.6-27B.
Synthetic Reddit: 19k synthetic QA pairs derived from a dump of r/FragReddit posts. We used Qwen-3.6-27B to filter useful posts, rephrase questions for clarity, and generate helpful and educational answers.
NER instructions: 17k NER tasks derived from 2 German NER datasets.

The mixture is designed to combine broad topical coverage with naturalness of German expression, complementing the information-dense pre-training corpus underlying the base model.

Usage

Boldt-1B-IT-Preview is designed for single-turn German-language instruction-following tasks. It was not fine-tuned for multi-turn conversations, and performance in multi-turn settings is not guaranteed. It uses a standard chat template and can be used as follows:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Boldt/Boldt-1B-IT-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Erkläre mir kurz, wie Quantencomputer funktionieren."}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

Limitations

Language: This model is optimized for German. Other languages are not supported.
Preview status: This model is released as a research preview. It may produce factually incorrect or inconsistent outputs. Not optimized for multi-turn dialogue.
Safety: We have not conducted systematic evaluations for toxic content, demographic biases, or harmful stereotypes. Quality filtering during pre-training may reduce some risks relative to unfiltered corpora but cannot eliminate them. Repeated multi-epoch exposure may amplify encoded biases. Users should exercise caution in sensitive applications.

Citation

@misc{boldt,
      title={Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling}, 
      author={Ansar Aynetdinov and Patrick Haller and Alan Akbik},
      year={2026},
      eprint={2604.28075},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.28075}, 
}