---
base_model:
- meta-llama/Llama-3.1-8B
library_name: transformers
license: mit
pipeline_tag: text-generation
---
# TokenButler
The collection of TokenButler models can be found [here](https://huggingface.co/collections/akhauriyash/tokenbutler-67cf181b5762d0d60e5f312b). To run the `meta-llama/Llama-3.1-8B` model, follow:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
question = "If millionaires have butlers, why don't million dollar language models have a butler too? I think its because "
model_name = "akhauriyash/Llama-3.1-8B-Butler"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
response = generator(question, max_new_tokens=200, do_sample=True, top_p=0.95, temperature=0.7)
print(response[0]['generated_text'][len(question):])
```
Note that the 'default' configured sparsity is 50%. Further, there is a 'sliding window' of 128 and 8 'anchor tokens'. To 'change' the sparsity, you can use the following function after loading the model. Please note that the 'fixed' is the only supported strategy at the moment, which 'fixes' the sparsity of each layer (except the first) at the 'pc' (percentage) mentioned. This can also be found at `test_hf.py`. Sliding window and anchor tokens can be changed in a similar manner.
```python
def set_sparsity(model, sparsity):
for module in model.modules():
if module.__class__.__name__.__contains__("AttentionExperimental"):
module.token_sparse_method = sparsity
module.set_token_sparsity()
return model
model = set_sparsity(model, "fixed_60pc")
```
# Predictor Architecture
# Custom Synthetic Task
