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--- |
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language: |
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- nl |
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license: cc-by-nc-4.0 |
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datasets: |
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- BramVanroy/alpaca-dolly-dutch |
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inference: false |
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base_model: tiiuae/falcon-40b |
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model-index: |
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- name: falcon-7b-ft-alpaca-cleaned-dutch |
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results: [] |
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--- |
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# falcon-40b-ft-alpaca-dolly-dutch |
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## Model description |
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This model is a fine-tuned version of [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [BramVanroy/alpaca-dolly-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-dolly-dutch) dataset. |
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See the original [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) for more information, intended use, and biases. |
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## Intended uses & limitations |
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This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes and experimentation. |
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## Example usage |
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In the example below, you see a query `Wat hoort er niet in dit rijtje thuis? Leg ook uit waarom.` ("What does not belong in the list? Explain why.") with given input "aap, muis, auto, vogel" ("monkey, mouse, car, bird"). |
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The model "replies" (cut off due to `max_new_tokens`): |
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> "Auto" hoort niet in het rijtje, omdat het geen levend wezen is. |
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> Een auto is een voertuig dat wordt aangedreven door een motor en wordt gebruikt om mensen en goederen van de ene plaats naar de andere te verplaatsen. Het is een machine gemaakt door mensen, in tegenstelling tot levende wezens zoals een aap, een muis of een vogel. |
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> Auto's zijn gemaakt van metalen, plastic en andere materialen, terwijl levende organismen bestaan uit cellen en weefsels. Auto's |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def format_alpaca_sample(instruction: str, input_text: str): |
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if len(input_text) >= 2: |
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text = f'''Hieronder staat een instructie `Instruction` die een taak beschrijft, gecombineerd met een invoer `Input` die verdere context biedt. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt. |
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### Instruction: |
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{instruction} |
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### Input: |
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{input_text} |
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### Response: |
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''' |
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else: |
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text = f'''Hieronder staat een instructie `Instruction` die een taak beschrijft. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt. |
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### Instruction: |
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{instruction} |
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### Response: |
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''' |
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return text |
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@torch.no_grad() |
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def generate(model, tokenizer, instruction: str, input_text: str = ""): |
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input_prompt = format_alpaca_sample(instruction, input_text) |
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inputs = tokenizer([input_prompt], return_tensors="pt") |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"].to(model.device), |
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attention_mask=inputs["attention_mask"].to(model.device), |
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max_new_tokens=128, |
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temperature=0.4, |
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num_beams=3, |
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no_repeat_ngram_size=4, |
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length_penalty=0.9, |
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early_stopping=True, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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).detach().to("cpu")[0] |
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return tokenizer.decode(generated_ids) |
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model_name = "BramVanroy/falcon-40b-ft-alpaca-dolly-dutch" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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load_in_4bit=True, |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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device_map="auto" |
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) |
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model.eval() |
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instruction = "Wat hoort er niet in dit rijtje thuis? Leg ook uit waarom." |
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input_text = "aap, muis, auto, vogel" |
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generation = generate(model, tokenizer, instruction, input_text) |
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``` |
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## Citation |
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If you want to refer to this model, you can cite the following: |
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Vanroy, B. (2023). Falcon 40B Finetuned on Dutch Translations of Alpca and Dolly. https://doi.org/10.57967/hf/0864 |
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```bibtext |
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@misc{vanroy2023falcon40b_instruct_dutch, |
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author = { Vanroy, Bram }, |
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title = { Falcon 40B Finetuned on Dutch Translations of Alpaca and Dolly}, |
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year = 2023, |
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url = { https://huggingface.co/BramVanroy/falcon-40b-ft-alpaca-dolly-dutch }, |
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doi = { 10.57967/hf/0864 }, |
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publisher = { Hugging Face } |
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} |
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``` |
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## Training and evaluation data |
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Trained on the synthetic [BramVanroy/alpaca-dolly-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-dolly-dutch) instruction dataset. |
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Therefore, commercial use of this model is forbidden. The model is intended for research purposes only. |
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- [Dolly 15k](https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch) (translated to Dutch) |
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- [Alpaca cleaned](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) (translated to Dutch) |
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## Training procedure |
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Trained with LoRA and merged before upload. The adapters are in the `adapters` branch. |
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Prompt template (where the input is optional and can be left out): |
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``` |
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Hieronder staat een instructie `Instruction` die een taak beschrijft, gecombineerd met een invoer `Input` die verdere context biedt. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt. |
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### Instruction: |
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{instruction} |
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### Input: |
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{input} |
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### Response: |
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{response} |
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``` |
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The loss was only calculated on the response prediction. |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 16 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 5 (but with early stopping) |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.1656 | 0.16 | 20 | 1.0107 | |
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| 0.9778 | 0.32 | 40 | 0.9711 | |
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| 1.0424 | 0.49 | 60 | 0.9512 | |
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| 0.9858 | 0.65 | 80 | 0.9415 | |
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| 0.9457 | 0.81 | 100 | 0.9341 | |
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| 1.0584 | 0.97 | 120 | 0.9277 | |
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| 1.0284 | 1.14 | 140 | 0.9372 | |
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| 0.8781 | 1.3 | 160 | 0.9295 | |
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| 0.9531 | 1.46 | 180 | 0.9267 | |
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| 0.9496 | 1.62 | 200 | 0.9226 | |
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| 0.9178 | 1.78 | 220 | 0.9192 | |
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| 1.0763 | 1.95 | 240 | 0.9154 | |
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| 0.9561 | 2.11 | 260 | 0.9423 | |
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| 0.7991 | 2.27 | 280 | 0.9368 | |
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| 0.8503 | 2.43 | 300 | 0.9363 | |
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| 0.8749 | 2.6 | 320 | 0.9299 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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