Update README.md

README.md

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 ## ChocoLlama-2-7B-instruct: Getting Started
 
 We here present **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. 
-Its base model, [ChocoLlama-2-7B-base](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-base), is a language-adapted version of Meta's Llama-2-7b, fine-tuned on a Dutch dataset of 104GB using LoRa.
+Its base model, [ChocoLlama-2-7B-base](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-base), is a language-adapted version of Meta's Llama-2-7b, fine-tuned on 32B Dutch Llama-2 tokens (104GB) using LoRa.
 
 Use the code below to get started with the model.
 
@@ -70,7 +70,7 @@ Hence, for any commercial applications, we recommend finetuning the base model o
 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.
 
 We provide 6 variants (of which 3 base and 3 instruction-tuned models):
-- **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 a Dutch dataset of 104GB using LoRa.
+- **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.
 - **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.
 - **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.
 - **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.
@@ -82,7 +82,7 @@ For benchmark results for all models, including compared to their base models an
 ### Model Description
 
 - **Developed by:** [Matthieu Meeus](https://huggingface.co/matthieumeeus97), [Anthony Rathé](https://huggingface.co/anthonyrathe)
-- **Funded by:** [Vlaams Supercomputer Centrum](https://www.vscentrum.be/), through a grant of apx. 40K GPU hours (NVIDIA H100-80GB)
+- **Funded by:** [Vlaams Supercomputer Centrum](https://www.vscentrum.be/), through a grant of apx. 40K GPU hours (NVIDIA A100-80GB)
 - **Language(s):** Dutch
 - **License:** cc-by-nc-4.0
 - **Finetuned from model:** [ChocoLlama-2-7B-base](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-base)
@@ -136,7 +136,7 @@ For both the SFT and DPO stage, we update all model weights and apply the same s
 - lr_scheduler_warmup_ratio: 0.1
 - num_epochs: 1
 
-Further, we leverage the publicly available [alignment handbook](https://github.com/huggingface/alignment-handbook) and use a set of 4 NVIDIA A100 (80 GB RAM) for both stages.
+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.
 
 ## Evaluation
 
@@ -171,4 +171,4 @@ For details, we refer to the paper and to our benchmark [ChocoLlama-Bench](https
 
 ### Compute Infrastructure
 
-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 H100 GPU's with 80 GB of VRAM.
+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.