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Model: BramVanroy/Llama-2-13b-chat-dutch Source: Original Platform
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README.md
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---
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pipeline_tag: text-generation
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tags:
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- conversational
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---
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# Llama-2-13b-chat-dutch
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> [!WARNING]
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> ⚠️ **NOTE 15/3/2024**: I do not recommend the use of this model. It was created with limited compute and data. Instead, try the much more powerful [Mistral-based GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra)!
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---
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This model is a fine-tuned version of [BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny](https://huggingface.co/BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny)
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on the [BramVanroy/dutch_chat_datasets](https://huggingface.co/datasets/BramVanroy/dutch_chat_datasets) dataset on a context of 4096 tokens.
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See the original [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) for more information, intended use, and biases.
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If you use this model or refer to it, please use the following citation:
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Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852)
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```bibtext
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@article{vanroy2023language,
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title={Language Resources for {Dutch} Large Language Modelling},
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author={Vanroy, Bram},
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journal={arXiv preprint arXiv:2312.12852},
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year={2023}
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}
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```
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## Usage
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```python
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from transformers import pipeline, Conversation
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chatbot = pipeline(
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"conversational",
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model="BramVanroy/Llama-2-13b-chat-dutch",
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model_kwargs={
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"device_map": "auto",
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"load_in_8bit": True
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}
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)
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# Ask a first question
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conversation = Conversation("Wat zijn enkele kleuren van de regenboog?")
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conversation = chatbot(conversation)
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# assistant: De regenboog bestaat uit zeven verschillende kleuren: rood, oranje, geel, groen, blauw, indigo en violet.
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# Ask a second question
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conversation.add_user_input("Interessant! Hoe worden die kleuren gevormd?")
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conversation = chatbot(conversation)
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print(conversation)
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# Conversation id: d8abbddb-6249-4699-b789-d1b42bb4fd71
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# user: Wat zijn enkele kleuren van de regenboog?
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# assistant: De regenboog bestaat uit zeven verschillende kleuren: rood, oranje, geel, groen, blauw, indigo en violet.
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# user: Interessant! Hoe worden die kleuren gevormd?
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# assistant: De kleuren van de regenboog worden gevormd door het breken van licht door waterdruppels in de atmosfeer. Elke druppel heeft een unieke grootte en vorm, waardoor het licht dat door de druppel gaat wordt gefragmenteerd en verschillende kleuren op de bodem van de druppel wordt weerkaatst.
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```
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## Model description
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I could not get the original Llama 2 13B to produce much Dutch, even though the description paper indicates that it was trained on a (small) portion of Dutch data. I therefore
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continued training the original Llama 2 13B checkpoint on Dutch data [in regular CLM](https://huggingface.co/BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny). In a second
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step I finetuned that model on a collection of synthetic (translated) instruction and chat datasets that I have [collected](https://huggingface.co/datasets/BramVanroy/dutch_chat_datasets).
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See their pages for licensing, usage, creation, and citation information.
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- https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch
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- https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch-baize
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- https://huggingface.co/datasets/BramVanroy/stackoverflow-chat-dutch
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- https://huggingface.co/datasets/BramVanroy/quora-chat-dutch
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This model is the result of that process. While not perfect by any means, it can perform reasonably well in Dutch depending on the prompts. It is also decent at helping with programming tasks.
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## Intended uses & limitations
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Depending on the prompt, the model can return good results considering that it is only 13B in size and was only marginally pretrained on Dutch. That being said, the
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model was not trained on human feedback and contains no safe-guards so it may produce unexpected and even offensive content depending on the query. The only attempt
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of a safe-guard is the default prompt that it was trained on, which was
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> Je bent een behulpzame, respectvolle en eerlijke assistent. Antwoord altijd zo behulpzaam mogelijk. Je antwoorden mogen geen schadelijke, onethische, racistische, seksistische, gevaarlijke of illegale inhoud bevatten. Zorg ervoor dat je antwoorden sociaal onbevooroordeeld en positief van aard zijn.\n\nAls een vraag nergens op slaat of feitelijk niet coherent is, leg dan uit waarom in plaats van iets niet correct te antwoorden. Als je het antwoord op een vraag niet weet, deel dan geen onjuiste informatie.
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This system message is automatically applied when you use a conversational pipeline or when you use tokenizer.apply_chat_template.
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Use this model with caution and at your own risk!
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Because the model was trained on synthetic data, translated with OpenAI's API, you cannot use this model to create a competitive product to theirs.
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## Training procedure
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Trained with 4096 tokens context length. The dataset was preprocessed so that as many as possible dialogs were put in a single batch, without disrupting
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dialogs. In other words, a dialog was never split up over different sequences or batches. During training, the human prompts were ignored in back propagation.
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Trained with LoRA targetting ["q_proj", "v_proj"] in 4 bit and merged before upload. Trained with Flash Attention as borrowed from [here](https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/utils/llama_patch.py).
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The adapters are in the `adapters` branch.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 64
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- total_eval_batch_size: 8
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.0193 | 0.09 | 20 | 1.1583 |
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| 0.9743 | 0.17 | 40 | 1.1339 |
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| 0.9159 | 0.26 | 60 | 1.1218 |
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| 0.9131 | 0.35 | 80 | 1.1153 |
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| 0.8816 | 0.44 | 100 | 1.1130 |
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| 0.8977 | 0.52 | 120 | 1.1069 |
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| 0.9061 | 0.61 | 140 | 1.1025 |
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| 0.8672 | 0.7 | 160 | 1.1024 |
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| 0.8956 | 0.79 | 180 | 1.0971 |
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| 0.8514 | 0.87 | 200 | 1.0995 |
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| 0.8357 | 0.96 | 220 | 1.0952 |
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| 0.8294 | 1.05 | 240 | 1.0964 |
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| 0.8531 | 1.13 | 260 | 1.0947 |
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| 0.8321 | 1.22 | 280 | 1.0951 |
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| 0.8365 | 1.31 | 300 | 1.0910 |
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| 0.8616 | 1.4 | 320 | 1.0894 |
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| 0.8397 | 1.48 | 340 | 1.0904 |
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| 0.861 | 1.57 | 360 | 1.0880 |
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| 0.8116 | 1.66 | 380 | 1.0871 |
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| 0.8285 | 1.74 | 400 | 1.0855 |
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| 0.8603 | 1.83 | 420 | 1.0856 |
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| 0.8126 | 1.92 | 440 | 1.0848 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu117
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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# [Open LLM Leaderboard Evaluation Results (English)](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BramVanroy__Llama-2-13b-chat-dutch)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 46.91 |
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| ARC (25-shot) | 59.3 |
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| HellaSwag (10-shot) | 81.45 |
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| MMLU (5-shot) | 55.82 |
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| TruthfulQA (0-shot) | 38.23 |
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| Winogrande (5-shot) | 76.64 |
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| GSM8K (5-shot) | 10.69 |
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| DROP (3-shot) | 6.28 |
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# Open LLM Leaderboard Evaluation Results (Dutch)
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Results can be found [here](https://huggingface.co/spaces/BramVanroy/open_dutch_llm_leaderboard)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 0.43 |
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| ARC (25-shot) | 0.38 |
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| HellaSwag (10-shot) | 0.56 |
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| MMLU (5-shot) | 0.35 |
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| TruthfulQA (0-shot) | 0.44 |
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all_results.json
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"epoch": 2.0,
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"eval_loss": 1.0847766399383545,
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"eval_runtime": 298.8617,
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"eval_samples": 630,
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"eval_samples_per_second": 2.108,
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"eval_steps_per_second": 0.264,
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"perplexity": 2.9587788718964534,
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"train_steps_per_second": 0.009
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config.json
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{
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"_name_or_path": "meta-llama/Llama-2-13b-hf",
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.31.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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"eval_steps_per_second": 0.264,
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"perplexity": 2.9587788718964534
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}
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
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|
||||
"content": "<s>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
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|
||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
40
tokenizer_config.json
Normal file
40
tokenizer_config.json
Normal file
@@ -0,0 +1,40 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
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|
||||
"special": true
|
||||
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|
||||
"1": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{%set system_message = 'Je bent een behulpzame, respectvolle en eerlijke assistent. Antwoord altijd zo behulpzaam mogelijk. Je antwoorden mogen geen schadelijke, onethische, racistische, seksistische, gevaarlijke of illegale inhoud bevatten. Zorg ervoor dat je antwoorden sociaal onbevooroordeeld en positief van aard zijn.\n\nAls een vraag nergens op slaat of feitelijk niet coherent is, leg dan uit waarom in plaats van iets niet correct te antwoorden. Als je het antwoord op een vraag niet weet, deel dan geen onjuiste informatie.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\n' + system_message + '\n<</SYS>>\n\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\n' + content.strip() + '\n<</SYS>>\n\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
8
train_results.json
Normal file
8
train_results.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"epoch": 2.0,
|
||||
"train_loss": 0.8817351038799536,
|
||||
"train_runtime": 50140.2452,
|
||||
"train_samples": 14670,
|
||||
"train_samples_per_second": 0.585,
|
||||
"train_steps_per_second": 0.009
|
||||
}
|
||||
2949
trainer_state.json
Normal file
2949
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user