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Model: ChocoLlama/Llama-3-ChocoLlama-8B-instruct Source: Original Platform
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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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base_model: ChocoLlama/Llama-3-ChocoLlama-8B-base
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datasets:
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- BramVanroy/ultrachat_200k_dutch
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- BramVanroy/stackoverflow-chat-dutch
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- BramVanroy/alpaca-cleaned-dutch
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- BramVanroy/dolly-15k-dutch
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- BramVanroy/no_robots_dutch
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- BramVanroy/ultra_feedback_dutch
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---
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<p align="center" style="margin:0;padding:0">
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<img src="./chocollama_logo.png" alt="ChocoLlama logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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</p>
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<div style="margin:auto; text-align:center">
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<h1 style="margin-bottom: 0">ChocoLlama</h1>
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<em>A Llama-2/3-based family of Dutch language models</em>
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</div>
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## Llama-3-ChocoLlama-8B-instruct: Getting Started
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We here present **ChocoLlama-2-7B-instruct**, an instruction-tuned version of Llama-3-ChocoLlama-8B-base, fine-tuned on a collection of Dutch translations of instruction-tuning datasets, using SFT followed by DPO.
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Its base model, [Llama-3-ChocoLlama-8B-base](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-base), is a language-adapted version of Meta's Llama-2-7b, fine-tuned on 32B Dutch Llama-2 tokens (104GB) using LoRa.
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained('ChocoLlama/Llama-3-ChocoLlama-8B-instruct')
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model = AutoModelForCausalLM.from_pretrained('ChocoLlama/Llama-3-ChocoLlama-8B-instruct', device_map="auto")
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messages = [
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{"role": "system", "content": "Je bent een artificiële intelligentie-assistent en geeft behulpzame, gedetailleerde en beleefde antwoorden op de vragen van de gebruiker."},
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{"role": "user", "content": "Jacques brel, Willem Elsschot en Jan Jambon zitten op café. Waar zouden ze over babbelen?"},
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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new_terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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eos_token_id=new_terminators,
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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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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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Note that the datasets used for instruction-tuning were translated using GPT-3.5/4, which means that this instruction-tuned model can not be used for commercial purposes.
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Hence, for any commercial applications, we recommend finetuning the base model on your own Dutch data.
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## Model Details
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ChocoLlama is a family of open LLM's specifically adapted to Dutch, contributing to the state-of-the-art of Dutch open LLM's in their weight class.
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We provide 6 variants (of which 3 base and 3 instruction-tuned models):
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- **ChocoLlama-2-7B-base** ([link](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-base)): A language-adapted version of Meta's Llama-2-7b, fine-tuned on 32B Dutch Llama-2 tokens (104GB) using LoRa.
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- **ChocoLlama-2-7B-instruct** ([link](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-instruct)): An instruction-tuned version of ChocoLlama-2-7B-base, fine-tuned on a collection of Dutch translations of instruction-tuning datasets, using SFT followed by DPO.
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- **ChocoLlama-2-7B-tokentrans-base** ([link](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-tokentrans-base)): A language-adapted version of Meta's Llama-2-7b, using a Dutch RoBERTa-based tokenizer. The token embeddings of this model were reinitialized using the token translation algorithm proposed by [Remy et al.](https://arxiv.org/pdf/2310.03477). The model was subsequently fine-tuned on the same Dutch dataset as ChocoLlama-2-7B-base, again using LoRa.
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- **ChocoLlama-2-7B-tokentrans-instruct** ([link](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-tokentrans-instruct)): An instruction-tuned version of ChocoLlama-2-7B-tokentrans-base, fine-tuned on the same dataset as ChocoLlama-2-7B-instruct, again using SFT followed by DPO.
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- **Llama-3-ChocoLlama-8B-base** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-base)): A language-adapted version of Meta's Llama-8-8B, fine-tuned on the same Dutch dataset as ChocoLlama-2-7B-base, again using LoRa.
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- **Llama-3-ChocoLlama-instruct** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-instruct)): An instruction-tuned version of Llama-3-ChocoLlama-8B-base, fine-tuned on the same dataset as ChocoLlama-2-7B-instruct, again using SFT followed by DPO.
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For benchmark results for all models, including compared to their base models and other Dutch LLMs, we refer to our paper [here](https://arxiv.org/pdf/2412.07633).
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### Model Description
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- **Developed by:** [Matthieu Meeus](https://huggingface.co/matthieumeeus97), [Anthony Rathé](https://huggingface.co/anthonyrathe)
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- **Funded by:** [Vlaams Supercomputer Centrum](https://www.vscentrum.be/), through a grant of apx. 40K GPU hours (NVIDIA A100-80GB)
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- **Language(s):** Dutch
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- **License:** cc-by-nc-4.0
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- **Finetuned from model:** [Llama-3-ChocoLlama-8B-instruct](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-instruct)
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### Model Sources
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- **Repository:** [on Github here](https://github.com/ChocoLlamaModel/ChocoLlama).
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- **Paper:** [on ArXiv here](https://arxiv.org/pdf/2412.07633).
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## Uses
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### Direct Use
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This is an instruction-tuned (SFT + DPO) Dutch model, optimized for Dutch language generation in conversational settings.
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For optimal behavior, we advice to only use the model with the correct chat template (see Python code above), potentially supported by a system prompt.
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### Out-of-Scope Use
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Use-cases requiring understanding or generation of text in languages other than Dutch: the dataset on which this model was fine-tuned does not contain data in languages other than Dutch, hence we expect significant catastrophic forgetting to have occured for English, which is the language Llama-2 was originally trained for.
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## Bias, Risks, and Limitations
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We have taken care to include only widely used and high-quality data in our dataset. Some of this data has been filtered by the original creators.
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However we did not explicitly conduct any additional filtering of this dataset with regards to biased or otherwise harmful content.
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## Training Details
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We adopt the same strategy as used to align GEITje-7B to [GEITje-7B-ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).
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First, we apply supervised finetuning (SFT), utilizing the data made available by [Vanroy](https://arxiv.org/pdf/2312.12852):
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- [BramVanroy/ultrachat_200k_dutch](https://huggingface.co/datasets/BramVanroy/ultrachat_200k_dutch)
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- [BramVanroy/no_robots_dutch](https://huggingface.co/datasets/BramVanroy/no_robots_dutch)
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- [BramVanroy/stackoverflow-chat-dutch](https://huggingface.co/datasets/BramVanroy/stackoverflow-chat-dutch)
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- [BramVanroy/alpaca-cleaned-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch)
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- [BramVanroy/dolly-15k-dutch](https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch)
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Next, we apply Direct Preference Optimization (DPO) to the SFT version of all the pretrained models we here develop,
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now utilizing a Dutch version of the data used to train Zephyr-7B-$\beta$, [BramVanroy/ultra_feedback_dutch](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch).
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For both the SFT and DPO stage, we update all model weights and apply the same set of hyperparameters to all models as used in GEITje-7B-ultra:
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- learning_rate: 5e-07
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 4
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- total_train_batch_size: 64
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- total_eval_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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Further, we leverage the publicly available [alignment handbook](https://github.com/huggingface/alignment-handbook) and use a set of 4 NVIDIA A100 (80 GB) for both stages.
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## Evaluation
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### Quantitative evaluation
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We have evaluated our models on several industry-standard Dutch benchmarks, translated from their original versions. The results can be found in the table below, together with results from several other prominent Dutch models.
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| Model | ARC | HellaSwag | MMLU | TruthfulQA | Avg. |
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|----------------------------------------------|----------------|----------------|----------------|----------------|----------------|
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| **Llama-3-ChocoLlama-instruct** | **0.48** | **0.66** | **0.49** | **0.49** | **0.53** |
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| llama-3-8B-rebatch | 0.44 | 0.64 | 0.46 | 0.48 | 0.51 |
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| llama-3-8B-instruct | 0.47 | 0.59 | 0.47 | 0.52 | 0.51 |
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| llama-3-8B | 0.44 | 0.64 | 0.47 | 0.45 | 0.5 |
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| Reynaerde-7B-Chat | 0.44 | 0.62 | 0.39 | 0.52 | 0.49 |
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| **Llama-3-ChocoLlama-base** | **0.45** | **0.64** | **0.44** | **0.44** | **0.49** |
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| zephyr-7b-beta | 0.43 | 0.58 | 0.43 | 0.53 | 0.49 |
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| geitje-7b-ultra | 0.40 | 0.66 | 0.36 | 0.49 | 0.48 |
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| **ChocoLlama-2-7B-tokentrans-instruct** | **0.45** | **0.62** | **0.34** | **0.42** | **0.46** |
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| mistral-7b-v0.1 | 0.43 | 0.58 | 0.37 | 0.45 | 0.46 |
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| **ChocoLlama-2-7B-tokentrans-base** | **0.42** | **0.61** | **0.32** | **0.43** | **0.45** |
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| **ChocoLlama-2-7B-instruct** | **0.36** | **0.57** | **0.33** | **0.45** | **0.43 |
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| **ChocoLlama-2-7B-base** | **0.35** | **0.56** | **0.31** | **0.43** | **0.41** |
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| llama-2-7b-chat-hf | 0.36 | 0.49 | 0.33 | 0.44 | 0.41 |
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| llama-2-7b-hf | 0.36 | 0.51 | 0.32 | 0.41 | 0.40 |
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On average, Llama-3-ChocoLlama-instruct surpasses the previous state-of-the-art on these benchmarks.
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### Qualitative evaluation
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In our paper, we also provide an additional qualitative evaluation of all models - which we empirically find more reliable.
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For details, we refer to the paper and to our benchmark [ChocoLlama-Bench](https://huggingface.co/datasets/ChocoLlama/ChocoLlama-Bench).
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### Compute Infrastructure
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All ChocoLlama models have been trained on the compute cluster provided by the [Flemish Supercomputer Center (VSC)](https://www.vscentrum.be/). We used 8 to 16 NVIDIA A100 GPU's with 80 GB of VRAM.
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## Citation
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If you found this useful for your work, kindly cite our paper:
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```
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@article{meeus2024chocollama,
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title={ChocoLlama: Lessons Learned From Teaching Llamas Dutch},
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author={Meeus, Matthieu and Rath{\'e}, Anthony and Remy, Fran{\c{c}}ois and Delobelle, Pieter and Decorte, Jens-Joris and Demeester, Thomas},
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journal={arXiv preprint arXiv:2412.07633},
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year={2024}
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}
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```
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config.json
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{
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"_name_or_path": "llama-2-nl/Meta-Llama-3-8B-lora-original",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"mlp_bias": false,
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"model_type": "llama",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": null,
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.41.2",
|
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"use_cache": false,
|
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"vocab_size": 128256
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}
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}
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||||
}
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||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user