初始化项目，由ModelHub XC社区提供模型
Model: Tweeties/tweety-7b-dutch-v24a Source: Original Platform
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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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library_name: transformers
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---
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[Pieter Delobelle](https://pieter.ai), [François Remy](https://fremycompany.com), [Miryam de Lhoneux](https://people.cs.kuleuven.be/~miryam.delhoneux/), [Thomas Demeester](https://tdmeeste.github.io)
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<p align="center">
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<img src="https://huggingface.co/DTAI-KULeuven/tweety-7b-dutch/resolve/main/tweety-7b-dutch.png?download=true" alt="Tweety-7b-dutch: A Dutch Large Language Model" width="20%">
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</p>
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[🇳🇱🇧🇪 Er is ook een Nederlandse readme](README_nl.md)
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# Model Card for tweety-7b-dutch
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tweety-7b-dutch is a foundation model with a focus on the Dutch language, incorporating a [Dutch tokenizer](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) for better understanding and generation of Dutch text. It's built on the mistral architecture, employing flash attention for efficient processing within a context window of 8192 tokens. Tweety-7b-dutch is trained on the [cleaned Dutch mC4 dataset](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), without instruction finetuning.
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## Model Details
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### Model Description
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Our tweety-7b-dutch model has an Apache 2.0 license, encouraging applications in research, content creation, and language analysis.
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- **Tokenizer:** Dutch, 50k tokens ([yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch))
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- **Pre-training data:** Scraped Dutch ([yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned))
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- **Context window**: 8196 tokens
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- **Training data**: 8.5B tokens
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- **Developed by:** KU Leuven and UGent
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- **Funded by:** KU Leuven BOF, VSC (Flemish Supercomputer Center), [Vlaams AI-onderzoeksprogramma](https://www.flandersairesearch.be/nl)
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- **Model type:** Foundation model
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- **License:** Apache 2.0
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## Uses
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As a base model, tweety-7b-dutch is primed for direct applications across text generation and understanding within the Dutch language.
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## Technical Specifications
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### Compute Infrastructure
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Training utilized Nvidia H100 and A100 GPUs. Inference is accessible on lower-end GPUs, basically any GPU capable of running mistral models.
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### Model Weights
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- This model was trained in bfloat16.
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- [GGUF weights](https://huggingface.co/BramVanroy/tweety-7b-dutch-v24a-GGUF) are released by Bram Vanroy.
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## Citation
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If you use this model, please cite our work as:
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```
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@article{tweeties2024,
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title = {Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP},
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author = {François Remy and Pieter Delobelle and Hayastan Avetisyan and Alfiya Khabibullina and Miryam de Lhoneux and Thomas Demeester},
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url = {https://arxiv.org/abs/2408.04303},
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year = {2024},
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note = {Accepted at COLM 2024}
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}
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```
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README_nl.md
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README_nl.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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library_name: transformers
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---
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[Pieter Delobelle](https://pieter.ai), [François Remy](https://fremycompany.com), [Miryam de Lhoneux](https://people.cs.kuleuven.be/~miryam.delhoneux/), [Thomas Demeester](https://tdmeeste.github.io)
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<p align="center">
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<img src="https://huggingface.co/DTAI-KULeuven/tweety-7b-dutch/resolve/main/tweety-7b-dutch.png?download=true" alt="Tweety-7b-dutch: Een Nederlands Groot Taalmodel" width="20%">
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</p>
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# Modelkaart voor tweety-7b-dutch
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tweety-7b-dutch is een Nederlands taalmodel, waarin een [Nederlandse tokenizer](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) is geïntegreerd voor betere representaties en generatie van Nederlandse tekst. Het is gebouwd op de Mistral-architectuur, maakt gebruik van flash attention en met een _context window_ van 8192 tokens. Tweety-7b-dutch is getraind op de [opgeschoonde Nederlandse mC4 dataset](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), zonder instructie-finetuning.
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## Modeldetails
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### Modelbeschrijving
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Ons tweety-7b-dutch model heeft een Apache 2.0 licentie, wat toepassingen aanmoedigt in onderzoek, contentcreatie en taalanalyse.
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- **Tokenizer:** Nederlands, 50k tokens ([yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch))
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- **Pre-training data:** Verzamelde Nederlandse teksten ([yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned))
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- **Contextvenster**: 8196 tokens
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- **Trainingsdata**: 8,5 miljard tokens
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- **Ontwikkeld door:** KU Leuven en UGent
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- **Gefinancierd door:** KU Leuven BOF, VSC (Vlaams Supercomputer Centrum), [Vlaams AI-onderzoeksprogramma](https://www.flandersairesearch.be/nl)
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- **Modeltype:** Foundationmodel
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- **Licentie:** Apache 2.0
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## Toepassingen
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Als basismodel is tweety-7b-dutch geschikt voor directe toepassingen in tekstgeneratie en -begrip binnen de Nederlandse taal.
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## Technische specificaties
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### Computerinfrastructuur
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De training maakte gebruik van Nvidia H100 en A100 GPU's. Inferentie is toegankelijk op minder krachtige GPU's, in principe elke GPU die in staat is om mistral-modellen te draaien.
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### Modelgewichten
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- Dit model werd getraind in bfloat16.
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- [GGUF-gewichten](https://huggingface.co/BramVanroy/tweety-7b-dutch-v24a-GGUF) worden uitgebracht door Bram Vanroy.
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## Citatie
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Als je dit model gebruikt, citeer dan ons werk als volgt:
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```
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@article{tweeties2024,
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title = {Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP},
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author = {François Remy and Pieter Delobelle and Hayastan Avetisyan and Alfiya Khabibullina and Miryam de Lhoneux and Thomas Demeester},
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url = {https://arxiv.org/abs/2408.04303},
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year = {2024},
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note = {Accepted at COLM 2024}
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}
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```
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"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
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"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
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||||
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||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.input_layernorm.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|endoftext|>",
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
63
tokenizer_config.json
Normal file
63
tokenizer_config.json
Normal file
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<mask>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"50256": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "</s>",
|
||||
"errors": "replace",
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
"model_max_length": 8196,
|
||||
"pad_token": "<pad>",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
BIN
tweety-7b-dutch.png
Normal file
BIN
tweety-7b-dutch.png
Normal file
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|
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50259
vocab.json
Normal file
50259
vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user