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Model: kamoo-ai/kamoo-one-135m Source: Original Platform
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README.md
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---
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language:
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- nl
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- dutch
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- nederlands
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- sovereign-ai
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- small-language-model
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- pretrained
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datasets:
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- HuggingFaceFW/fineweb-2
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- GPT-NL/GPT-NL_Public_Corpus
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- wikimedia/wikipedia
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---
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# kamoo-one-135m
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**Een klein, soeverein Nederlands taalmodel — vanaf nul getraind, open tot op de laatste token.**
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kamoo-one-135m is het instapmodel van de kamoo-one-familie: een decoder-only
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transformer van 135M parameters, vanaf nul getraind op 7,3 miljard Nederlandse
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tokens met een eigen Nederlandse 32k-tokenizer. Geen fine-tune van een
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bestaand model, geen vertaald bijproduct: elke parameter is op Nederlands
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getraind, op eigen hardware in Nederland.
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⚠️ **Dit is een basismodel** (geen instruct/chat-variant). Het zet tekst
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voort en is bedoeld als fundament voor fine-tuning op afgebakende
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Nederlandse teksttaken. Een instruct-variant volgt.
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## Gebruik
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("kamoo-ai/kamoo-one-135m")
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model = AutoModelForCausalLM.from_pretrained("kamoo-ai/kamoo-one-135m")
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prompt = "De gemeente heeft besloten dat"
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out = model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=60)
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print(tok.decode(out[0]))
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```
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Of met vLLM:
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```bash
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vllm serve kamoo-ai/kamoo-one-135m --max-model-len 1024
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```
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## Architectuur
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|---|---|
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| Parameters | 135M (125,9M excl. tied lm_head) |
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| Architectuur | decoder-only, Llama-compatibel (RMSNorm, RoPE, GQA, SwiGLU) |
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| Dimensie / lagen | 768 / 16 |
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| Attention | GQA, 12 heads / 4 kv-heads |
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| FFN | 2.048 |
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| Context | 1.024 tokens |
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| Tokenizer | kamoo-bpe-32k (32.768, puur Nederlands) |
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| Precisie | bfloat16 |
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## Trainingsdata: het v0.8-corpus
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7,3 miljard tokens (geschat op 4,45 tekens per token), 100% Nederlands,
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12 bronnen met per bron gedocumenteerde licentie en herkomst. Verdeling
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uit het trainingsrapport:
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| Categorie | Tokens | Aandeel | Bronnen |
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|---|---|---|---|
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| Webteksten | 4,9 mld | 67,4% | FineWeb-2 NL (hq + full), ODC-BY |
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| Kranten | 0,8 mld | 11,1% | KB open kranten via GPT-NL, CC-BY-4.0 |
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| Overheid & recht | 0,65 mld | 9,0% | rechtspraak, officiële bekendmakingen, Woo-documenten, Tweede Kamer, raadsinformatie (GPT-NL, CC-BY-4.0) |
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| Wikipedia | 0,57 mld | 7,9% | Nederlandse Wikipedia, CC-BY-SA |
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| Literatuur | 0,31 mld | 4,3% | DBNL en Project Gutenberg, publiek domein |
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| Onderwijs | 0,02 mld | 0,3% | Wikiwijs via GPT-NL, CC-BY-4.0 |
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De filterpipeline (taal, kwaliteit, OCR-ruis, per bron gekalibreerd,
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exacte en near-dedup) is gedocumenteerd; elk filterbesluit staat met
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motivatie in versiebeheer. Er zit geen synthetische data in de pretrain.
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## Metingen
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- **Validatie-perplexity**: 22,1 op held-out Nederlandse tekst uit dezelfde
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corpusverdeling (eigen tokenizer; perplexity is alleen vergelijkbaar
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binnen dezelfde tokenizer).
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- **Tokenizer-efficiëntie**: een identieke Nederlandse alinea (388 tekens,
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eerste alinea van het Wikipedia-artikel "Nederland") kost 73 kamoo-tokens
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tegen 88 bij de tokenizer van ChatGPT (o200k_base) en 115 bij OLMo 2,
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met een vocabulaire dat 6× kleiner is. Alinea, script en meetlog:
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[kamoo-ai/tokenizer-benchmark](https://huggingface.co/datasets/kamoo-ai/tokenizer-benchmark).
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## Grenzen, eerlijk
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- Geen brede parametrische feitenkennis: feiten horen uit meegegeven
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context of documenten te komen.
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- Geen meerstaps-redeneren zoals grote frontier-modellen.
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- Alleen Nederlands (bewuste keuze).
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- Context is 1.024 tokens; knip langere tekst in delen.
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- Als basismodel volgt het geen instructies; het zet tekst voort.
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## Herkomst en AI Act
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Vanaf nul getraind op eigen hardware in Nederland (~3×10¹⁹ FLOPs
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trainingscompute; ruim onder de indicatieve GPAI-drempel van 10²³ FLOPs
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uit de EU AI Act-richtsnoeren). Deze model card en het
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databronnenoverzicht publiceren we vrijwillig.
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## English summary
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kamoo-one-135m is a 135M-parameter decoder-only Dutch language model,
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trained from scratch on 7.3B exclusively Dutch tokens with a custom Dutch
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32k BPE tokenizer, on our own hardware in the Netherlands. It is a
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base model (not instruction-tuned), Llama-compatible, Apache-2.0 licensed,
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with fully documented training-data provenance per source. Built by
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[kamoo](https://kamoo.nl) as the entry-level tier of a sovereign Dutch
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model family.
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---
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*kamoo · [kamoo.nl](https://kamoo.nl) · [platform.kamoo.nl](https://platform.kamoo.nl) · gemaakt en getraind in Nederland*
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"model_type": "llama",
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"vocab_size": 32768,
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"hidden_size": 768,
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"intermediate_size": 2048,
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"num_hidden_layers": 16,
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"num_attention_heads": 12,
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"num_key_value_heads": 4,
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"head_dim": 64,
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"max_position_embeddings": 1024,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"hidden_act": "silu",
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"tie_word_embeddings": true,
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"attention_bias": false,
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"mlp_bias": false,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"torch_dtype": "bfloat16"
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}
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generation_config.json
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{
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"bos_token_id": 0,
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"eos_token_id": 0,
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"do_sample": true,
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"temperature": 0.8,
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"top_p": 0.95,
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"repetition_penalty": 1.3,
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"max_new_tokens": 256
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:2cd522e9616fcd6f6d0f296b558a86dea0cf12f93e6b73431c8d74038ab1185f
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size 251725264
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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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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"unk_token": "<|endoftext|>",
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"model_max_length": 1024,
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"clean_up_tokenization_spaces": false,
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"chat_template": "{%- for message in messages -%}{%- if message.role == 'system' -%}{{ message.content }}\n\n{%- elif message.role == 'user' -%}Vraag: {{ message.content }}\n\n{%- elif message.role == 'assistant' -%}Antwoord: {{ message.content }}\n\n{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}Antwoord:{%- endif -%}"
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}
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