Text Generation
Transformers
Safetensors
Dutch
English
qwen3
dutch
nederlands
instruction-tuned
conversational
question-answering
summarization
text-generation-inference
Instructions to use ToetsChecker-Research/boterham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToetsChecker-Research/boterham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ToetsChecker-Research/boterham") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ToetsChecker-Research/boterham") model = AutoModelForCausalLM.from_pretrained("ToetsChecker-Research/boterham", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ToetsChecker-Research/boterham with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ToetsChecker-Research/boterham" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ToetsChecker-Research/boterham", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ToetsChecker-Research/boterham
- SGLang
How to use ToetsChecker-Research/boterham 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 "ToetsChecker-Research/boterham" \ --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": "ToetsChecker-Research/boterham", "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 "ToetsChecker-Research/boterham" \ --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": "ToetsChecker-Research/boterham", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ToetsChecker-Research/boterham with Docker Model Runner:
docker model run hf.co/ToetsChecker-Research/boterham
Upload folder using huggingface_hub
Browse files- README.md +52 -7
- config.json +30 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +18 -0
README.md
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---
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license: apache-2.0
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language:
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- nl
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- en
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datasets:
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- ToetsChecker-Research/beleg
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tags:
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- pretrained
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- pytorch
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---
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#
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Dit is
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##
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---
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language:
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- nl
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- en
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license: apache-2.0
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datasets:
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- ToetsChecker-Research/beleg
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- nederlands
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- dutch
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- pretrained
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---
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# boterham-0.5B
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Een Nederlandstalig taalmodel, getraind op uitsluitend rechtmatig
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verkregen materiaal: publiek domein en permissief gelicentieerde
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bronnen. Geen geschraapt web, geen destillatie uit een ander model.
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De dataset waarop het draait staat los gepubliceerd als
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[`ToetsChecker-Research/beleg`](https://huggingface.co/datasets/ToetsChecker-Research/beleg),
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met per document de herkomst, de licentie en de ophaaldatum. Dat
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corpus is het eigenlijke product; een model veroudert binnen een jaar
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en een goed gedocumenteerde dataset niet.
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## Wat het is
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|---|---|
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| parameters | 488,3M (447,4M zonder embeddings) |
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| architectuur | decoder-only, GQA, RoPE, SwiGLU, RMSNorm, QK-norm |
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| contextlengte | 2048 tokens |
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| vocabulaire | 32,002 BPE, byte-level |
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| val-loss na finetuning | 0.8348 |
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## Hoe het getraind is
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Daarna gefinetuned op instructiedata die uit datzelfde corpus is afgeleid, 2.0 epochs, met het verlies alleen op het antwoord.
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Alle taken in de instructiedata zijn afgeleid uit echte documenten en
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achteraf gecontroleerd: een extractief antwoord moet letterlijk in de
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context staan, een onthoudingsvoorbeeld moet aantoonbaar niet in de
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context staan, en een samenvatting moet korter zijn dan de bron. Wat
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niet te controleren viel is niet meegegaan.
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## Wat het niet kan
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Dit is een model van een halve miljard parameters, getraind op een
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budget in miljarden tokens waar vergelijkbare modellen er biljoenen
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zien. Verwacht geen brede wereldkennis en geen betrouwbare feiten over
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personen, datums of getallen zonder context erbij. Het is gebouwd om
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te werken met een document in de prompt, niet om uit het hoofd te
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antwoorden.
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Losse conversatie en creatief schrijven zitten nauwelijks in de
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trainingsdata, want daar bestaat geen rechtmatige Nederlandstalige
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bron van omvang voor. Dat is een bewuste keuze en geen omissie.
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## Licentie en herkomst
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Het model staat onder Apache 2.0. De onderliggende teksten zijn
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publiek domein (artikel 11 Auteurswet voor overheidspublicaties en
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rechtspraak) of permissief gelicentieerd (CC BY, CC BY-SA, CC0).
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Per document staat de herkomst in de datasheet bij de dataset.
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"model_type": "qwen3",
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 18,
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"pad_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 1280,
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"initializer_range": 0.02,
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"intermediate_size": 3456,
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"max_position_embeddings": 2048,
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"max_window_layers": 26,
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"num_attention_heads": 20,
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"num_hidden_layers": 26,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 100000.0,
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"sliding_window": null,
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"use_sliding_window": false,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"use_cache": true,
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"vocab_size": 32002
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}
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generation_config.json
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{
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"bos_token_id": 1,
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"eos_token_id": 18,
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"pad_token_id": 0,
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"do_sample": false,
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"max_new_tokens": 256
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cd2788645b99179851014b0f0ec53f9d6ce76531086d8a46875932f6b7ec4064
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size 1953300248
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"eos_token": "<|einde|>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"tokenizer_class": "PreTrainedTokenizerFast",
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"model_max_length": 2048,
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"bos_token": "<s>",
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"eos_token": "<|einde|>",
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"pad_token": "<pad>",
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"unk_token": "<unk>",
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"clean_up_tokenization_spaces": false,
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"additional_special_tokens": [
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"<|gebruiker|>",
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"<|boterham|>",
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"<|systeem|>",
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"<|einde|>",
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"<|denken|>",
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"<|einde_denken|>"
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],
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"chat_template": "{% for bericht in messages %}{% if bericht['role'] == 'system' %}<|systeem|>{{ '\\n' + bericht['content'] + '\\n' }}{% elif bericht['role'] == 'user' %}<|gebruiker|>{{ '\\n' + bericht['content'] + '\\n' }}{% else %}<|boterham|>{{ '\\n' + bericht['content'] }}<|einde|>{% endif %}{% endfor %}{% if add_generation_prompt %}<|boterham|>{{ '\\n' }}{% endif %}"
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}
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