McGill-NLP/LLM2Vec-Gen-Qwen3-4B

LLM2Vec-Gen: Generative Embeddings from Large Language Models

LLM2Vec-Gen is a recipe to train interpretable, generative embeddings that encode the potential answer of an LLM to a query rather than the query itself.

• Repository: https://github.com/McGill-NLP/llm2vec-gen
• Paper: https://arxiv.org/abs/2603.10913

Installation

pip install llm2vec-gen

Usage

Load a pretrained model:

import torch
from llm2vec_gen import LLM2VecGenModel

model = LLM2VecGenModel.from_pretrained("McGill-NLP/LLM2Vec-Gen-Qwen3-8B")

As an example, you can use the model for retrieval using the following code snippet:

q_instruction = "Generate a passage that best answers this question: "
d_instruction = "Summarize the following passage: "

queries = [
  "where do polar bears live and what's their habitat",
  "what does disk cleanup mean on a computer"
]
q_reps = model.encode([q_instruction + q for q in queries])

documents = [
  "Polar bears live throughout the circumpolar North in the Arctic, spanning across Canada, Alaska (USA), Russia, Greenland, and Norway. Their primary habitat is sea ice over the continental shelf, which they use for hunting, mating, and traveling. They are marine mammals that rely on this environment to hunt seals.",
  "Disk Cleanup is a built-in Windows tool that frees up hard drive space by scanning for and deleting unnecessary files like temporary files, cached data, Windows updates, and items in the Recycle Bin. It improves computer performance by removing \"junk\" files, which can prevent the system from running slowly due to low storage.",
]
d_reps = model.encode([d_instruction + d for d in documents])

# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))

print(cos_sim)
"""
tensor([[0.8750, 0.1182],
        [0.0811, 0.9336]])
"""

Note that in all examples, the instructions should be as if you are generating the answer to the input.
You may find other examples to try LLM2Vec-Gen in other tasks (e.g., classification and clustering) in the paper's GitHub repository.

LLM2Vec-Gen provides interpretable embeddings. You can use the following code to decode the content embedded in the embeddings:

_, recon_hidden_states = model.encode("what does disk cleanup mean on a computer", get_recon_hidden_states=True)
# recon_hidden_states: torch.Tensor with shape (1, compression token size, hidden_dim)

answer = model.generate(recon_hidden_states=recon_hidden_states, max_new_tokens=55)

print(answer)
"""
* **\n\n**Disk Cleanup** is a built-in utility in Windows that helps you **free up disk space** by **removing unnecessary files** from your computer. It is designed to clean up temporary files, system cache, and other files that are no longer needed.\n\n
"""

This code snippet will return the answer of the LLM2Vec-Gen model generated from the generative embeddings of the input (recon_hidden_states).

Questions

If you have any question about the code, feel free to email Parishad (parishad.behnamghader@mila.quebec) and Vaibhav (vaibhav.adlakha@mila.quebec).

Citation

If you use our code, models, or data, please cite the LLM2Vec-Gen paper.

@article{behnamghader2026llm2vecgen,
  title={LLM2Vec-Gen: Generative Embeddings from Large Language Models},
  author={BehnamGhader, Parishad and Adlakha, Vaibhav and Schmidt, Fabian David and Chapados, Nicolas and Mosbach, Marius and Reddy, Siva},
  journal={arXiv preprint: arXiv:2603.10913},
  year={2026}
  url={https://arxiv.org/abs/2603.10913}
}