---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-1.7B
tags:
- memsft
- memory-decoder
- biology
- biology-instructions
- qwen3
---

# MemSFT-Qwen3-Bio-Memory-1.7B

<p align="center">
  <a href="https://arxiv.org/abs/2607.25614">📄 Paper</a> •
  <a href="https://github.com/LUMIA-Group/MemSFT">💻 GitHub</a> •
  <a href="https://huggingface.co/collections/Jiarui-Wang/memsft">🤗 HF Collection</a>
</p>

## Introduction

MemSFT specializes modern large language models with an external parametric
memory. This checkpoint contains the Qwen3-1.7B memory trained on
Biology-Instructions. The memory learns to approximate retrieval-based teacher
distributions over domain SFT data. At each decoding step, a learned
token-level router combines the next-token distributions of the frozen base
model and memory. This checkpoint is an auxiliary memory, not a standalone
chat model. Its key advantages are:

- **Plug-and-Play:** Attaches to a frozen backbone without modifying its
  parameters or architecture.
- **Strong Specialization:** Improves domain performance with negligible
  degradation in general capabilities.
- **Cross-Scale Reuse:** The MemSFT design supports reuse across compatible
  Qwen3 backbones without retraining the memory for each backbone.

## Quick Start

This example pairs the 1.7B memory with the Qwen3-14B backbone used in the
paper. It requires a CUDA GPU with sufficient memory to load both models in
BF16.

### 1. Install

```bash
git clone https://github.com/LUMIA-Group/MemSFT.git
cd MemSFT

conda create -n memsft-generate python=3.10 pip -y
conda activate memsft-generate
python -m pip install -e .
python -m pip install \
  "torch>=2.4,<2.7" \
  "transformers==4.51.3" \
  "huggingface-hub==0.35.3" \
  "accelerate>=0.34,<2"
```

### 2. Load the base, memory, and router

```python
from pathlib import Path

import torch
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer

from memsft.router.adaptive_memdec import AdaptiveMemoryDecoder

device = torch.device("cuda:0")
base_id = "Qwen/Qwen3-14B"
memory_id = "Jiarui-Wang/MemSFT-Qwen3-Bio-Memory-1.7B"
router_repo = "Jiarui-Wang/MemSFT-Qwen3-Routers"
router_subdir = "Qwen3-14B-Bio-M1.7B-Router"

router_root = snapshot_download(
    repo_id=router_repo,
    revision="v1.0.0",
    allow_patterns=[f"{router_subdir}/*"],
)
router_path = str(Path(router_root) / router_subdir)

tokenizer = AutoTokenizer.from_pretrained(
    base_id,
    revision="40c069824f4251a91eefaf281ebe4c544efd3e18",
)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    revision="40c069824f4251a91eefaf281ebe4c544efd3e18",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
).to(device).eval()
memory = AutoModelForCausalLM.from_pretrained(
    memory_id,
    revision="v1.0.0",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
).to(device).eval()

vocab_size = len(tokenizer)
base.resize_token_embeddings(vocab_size)
memory.resize_token_embeddings(vocab_size)
base.requires_grad_(False)
memory.requires_grad_(False)
model = AdaptiveMemoryDecoder(
    base_lm=base,
    knn_generator=memory,
    router_path=router_path,
    router_device=device,
).eval()
model.set_tokenizer(tokenizer)
```

### 3. Generate

```python
sequence = (
    "MKSILIEKPNQLAIVEREIPTPSAGEVRVKVKLAGICGSDSHIYRGHNPFAKYPRVIGHEFFGVIDAV"
    "GEGVESARVGERVAVDPVVSCGHCYPCSIGKPNVCTTLAVLGVHADGGFSEYAVVPAKNAWKIPEAVA"
    "DQYAVMIEPFTIAANVTGHGQPTENDTVLVYGAGPIGLTIVQVLKGVYNVKNVIVADRIDERLEKAKE"
    "SGADWAINNSQTPLGEIFTEKGIKPTLIIDAACHPSILKEAVTLASPAARIVLMGFSSEPSEVIQQGI"
    "TGKELSIFSSRLNANKFPIVIDWLSKGLIKPEKLITHTFDFQHVADAISLFEQDQKHCCKVLLTFSE"
)
prompt = (
    r"<PROTEIN> "
    + sequence
    + r" </PROTEIN> What is the EC number associated with the enzymatic "
    r"function of this protein? Please put the final enzyme within \boxed{} "
    r"using an EC number such as ECx.x.x.x, and separate multiple entries "
    r"with commas."
)

messages = [{"role": "user", "content": prompt}]
prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt_text, return_tensors="pt").to(device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        do_sample=False,
        max_new_tokens=32,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
    )

answer = tokenizer.decode(
    output_ids[0, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)
print(answer)
```

**Example output:**

```text
\boxed{EC1.1.1.-}
```

UniProtKB annotates *Escherichia coli* K-12 RspB
([P38105](https://www.uniprot.org/uniprotkb/P38105/entry)) with EC
`1.1.1.-`. For comparison, using the same prompt and deterministic generation
configuration, Qwen3-14B alone predicts `EC 4.2.1.22`. The outputs were
reproduced in BF16 on NVIDIA A800 80GB GPUs.

## Performance

The memory-scale experiment uses the same Biology-Instructions training data,
Qwen3-8B retrieval teacher, and frozen Qwen3-14B backbone across memory sizes.

| Configuration | Memory size | Biology-Instructions ↑ |
|---|---:|---:|
| Qwen3-14B | — | 6.64 |
| Qwen3-14B + MemSFT | 1.7B | 30.38 |

The paper reports a General average range of 83.23–83.62 across the 1.7B, 4B,
and 8B memory configurations, rather than a separate General average for this
checkpoint.

## Compatible Pairing

- base: [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B)
- memory: `Jiarui-Wang/MemSFT-Qwen3-Bio-Memory-1.7B`
- router:
  `Jiarui-Wang/MemSFT-Qwen3-Routers/Qwen3-14B-Bio-M1.7B-Router`

MemSFT prefers tensor-only `.safetensors` router checkpoints. Legacy `.pt`
checkpoints should be loaded only from trusted sources; the MemSFT loader uses
PyTorch's restricted `weights_only=True` mode for compatibility.

## Intended Use and Limitations

This checkpoint is intended for reproducing the MemSFT memory-scale
experiment and for augmenting Qwen3-14B on Biology-Instructions tasks. It
should be used with the matching Qwen3-14B/Bio/M1.7B router. This 1.7B memory
was not evaluated with the other backbone sizes in the paper. Performance
outside the evaluated pairing and domain has not been established.

## License

This MemSFT checkpoint is released under the Apache License 2.0. Upstream
models, software, and datasets remain subject to their respective licenses
and terms.

## Citation

If you find MemSFT helpful in your research, please consider citing:

```bibtex
@misc{wang2026memsftmitigatingalignmenttax,
      title={MemSFT: Mitigating Alignment Tax with an External Parametric Memory},
      author={Jiarui Wang and Xiang Shi and Jiaqi Cao and Rubin Wei and Xiquan Wang and Hao Sun and Jingzhi Wang and Zhiqi Yang and Qipeng Guo and Bowen Zhou and Zhouhan Lin},
      year={2026},
      eprint={2607.25614},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2607.25614}, 
}
```

## Contact

For questions and discussions, feel free to email
[wangjiarui1@sjtu.edu.cn](mailto:wangjiarui1@sjtu.edu.cn).