How to calculate embeddings with Llama-2 model
hi,I would like to calculate embeddings using a Llama-2 model and HuggingFaceEmbedding embedding class:from llama_index.embeddings import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(model_name="meta-llama/Llama-2-7b-chat-hf")
embeddings = embed_model.get_text_embedding("Hello World!")
print(len(embeddings))
print(embeddings[:5])But I get the following exception which I dont know how to bypass:Using pad_token, but it is not set yet.
Traceback (most recent call last):
  File "/home/ttpuser/chatgpt/embeddings-test/test.py", line 4, in <module>
    embeddings = embed_model.get_text_embedding("Hello World!")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/llama_index/embeddings/base.py", line 185, in get_text_embedding
    text_embedding = self._get_text_embedding(text)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/llama_index/embeddings/huggingface.py", line 184, in _get_text_embedding
    return self._embed([text])[0]
           ^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/llama_index/embeddings/huggingface.py", line 146, in _embed
    encoded_input = self._tokenizer(
                    ^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2602, in __call__
    encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2688, in _call_one
    return self.batch_encode_plus(
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2870, in batch_encode_plus
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ttpuser/.pyenv/versions/3.11.5/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2507, in _get_padding_truncation_strategies
    raise ValueError(
ValueError: Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.Does someone knows how to go around this?Thank you a lot!Ruben
2023-10-02T17:37:30Z
I tried the following with another model and got past the error but the embeddings were always the same no matter what the text.https://github.com/UKPLab/sentence-transformers/issues/1418from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('meta-llama/Llama-2-7b-chat-hf')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf')
tokenizer.pad_token = tokenizer.eos_token
embed_model = HuggingFaceEmbedding(model=model, model_name='meta-llama/Llama-2-7b-chat-hf', tokenizer=tokenizer, tokenizer_name='meta-llama/Llama-2-7b-chat-hf')
2023-10-03T06:21:55Z
I don’t know if his helps but try using sentence - transformer for embedding  plus its fast and lightweight  , it works really well , I too tried generating embeddings with llama 2 but failed , but sentence - transformer’s all-MiniLM-L12-v2  worked just as good as I had hoped I needed.https://www.sbert.net/u can run it on collab too , no additional resources gpu etc.
2023-10-31T00:20:11Z
Hi@gasparuben, AnglE-LLaMA is a good choice to generate LLaMA embedding. It has achieved state-of-the-art performance on the STS benchmark.GitHub:GitHub - SeanLee97/AnglE: Angle-optimized Text Embeddings | 🔥 New SOTAHF:SeanLee97/angle-llama-7b-nli-v2 · Hugging FaceUsage:python -m pip install -U angle-embfrom angle_emb import AnglE, Prompts

# init
angle = AnglE.from_pretrained('NousResearch/Llama-2-7b-hf', pretrained_lora_path='SeanLee97/angle-llama-7b-nli-v2')

# set prompt
print('All predefined prompts:', Prompts.list_prompts())
angle.set_prompt(prompt=Prompts.A)
print('prompt:', angle.prompt)

# encode text
vec = angle.encode({'text': 'hello world'}, to_numpy=True)
print(vec)
vecs = angle.encode([{'text': 'hello world1'}, {'text': 'hello world2'}], to_numpy=True)
print(vecs)