Instructions to use Unbabel/TowerInstruct-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unbabel/TowerInstruct-7B-v0.1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Unbabel/TowerInstruct-7B-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Unbabel/TowerInstruct-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("Unbabel/TowerInstruct-7B-v0.1") - Notebooks
- Google Colab
- Kaggle
Question regarding added tokens vs llama base
#7
by vince62s - opened
Hello,
I have some questions regarding the 7 added tokens.
Are embeddings learned at finetuning time or this just a "pre/post" processing usage ?
Also can you clarify the meaning of those:
Hey there,
The added tokens are there for flexibility if you want to fine-tune it for some specific use-case (e.g., MASK, CLS tokens). We only explicitly used at all times during the SFT the <|im_end|> (redefined as the eos token) and the <|im_start|> tokens.
They are learned at finetuning time.
nunonmg changed discussion status to closed