---
license: cc-by-4.0
---

# Finetuned `Gemma-2-2B` for generating subspaces given any natural language descriptions for `Gemma-2-9B-it`

In the AxBench paper, we finetuned a subspace generator. The subspace generator is a hyper-network that will generate a subspace for you given a concept description in natural language. **High-quality subspace generator can bypass all dictionary training!**

## How to use the subspace generator?

```py
import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer

class RegressionWrapper(torch.nn.Module):
    def __init__(self, base_model, hidden_size, output_dim):
        super().__init__()
        self.base_model = base_model
        self.regression_head = torch.nn.Linear(hidden_size, output_dim)

    def forward(self, input_ids, attention_mask):
        outputs = self.base_model.model(
            input_ids=input_ids, 
            attention_mask=attention_mask,
            output_hidden_states=True,
            return_dict=True
        )
        last_hiddens = outputs.hidden_states[-1]
        last_token_representations = last_hiddens[:, -1]
        preds = self.regression_head(last_token_representations)
        preds = F.normalize(preds, p=2, dim=-1)
        return preds

base_model = AutoModelForCausalLM.from_pretrained(
    f"google/gemma-2-2b", torch_dtype=torch.bfloat16)
base_tokenizer = AutoTokenizer.from_pretrained(
    f"google/gemma-2-2b", model_max_length=512)

subspace_gen = RegressionWrapper(
    base_model, hidden_size, output_dim).bfloat16().to("cuda")
subspace_gen.load_state_dict(torch.load('model.pth'))

your_new_concept = "terms related to Stanford University"

inputs = base_tokenizer(your_new_concept, return_tensors="pt").to("cuda")
input_ids, attention_mask = inputs["input_ids"], inputs["attention_mask"]
subspace_gen(input_ids, attention_mask)[0]
```