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---
language:
- en
- zh
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
# BlockFFN-3B-SFT-EAGLE
This is the 3B BlockFFN model used in the paper *BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity* for acceleration tests.
**BlockFFN** introduces a novel Mixture-of-Experts (MoE) architecture designed for efficient inference, particularly on end-side devices. It aims to achieve high token-level and chunk-level sparsity, making it acceleration-friendly and compatible with techniques like speculative decoding. This model is based on the [paper](https://arxiv.org/pdf/2507.08771).
For the full codebase and more details, visit the official [GitHub repository](https://github.com/thunlp/BlockFFN).
### Usage
You can easily load and use this model with the Hugging Face `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
model_name = "SparseLLM/BlockFFN-3B-SFT-EAGLE"
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16, # or torch.float16 if bfloat16 is not supported
device_map="auto",
trust_remote_code=True,
)
# Create a text generation pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
# Generate text
prompt = "The quick brown fox jumps over the lazy"
output = pipe(prompt, max_new_tokens=50, do_sample=True, temperature=0.7)
print(output[0]['generated_text'])
```
### Citation
If you find our work useful for your research, please kindly cite our paper as follows:
```
@article{song2025blockffn,
title={{BlockFFN}: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity},
author={Chenyang Song and Weilin Zhao and Xu Han and Chaojun Xiao and Yingfa Chen and Yuxuan Li and Zhiyuan Liu and Maosong Sun},
journal={arXiv preprint arXiv:2507.08771},
year={2025},
url={https://arxiv.org/pdf/2507.08771},
} |