LiveMem-SFT
LiveMem-4B-SFT uses a Qwen3-4B-Instruct-2507 backbone augmented with a parallel Gated DeltaNet 2 (GDN2) recurrent memory path in every decoder layer:
layer output = Qwen3 attention output + GDN2 memory output
The checkpoint is the supervised fine-tuned model used as the initialization
for chen-l/LiveMem-RL. It supports a maximum configured context length of
262,144 tokens. Actual usable context depends on GPU memory and inference
backend.
Transformers usage
LiveMem uses custom model code and GDN2 Triton kernels. A CUDA environment is required for inference.
pip install -r requirements.txt
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "chen-l/LiveMem-4B-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
dtype=torch.bfloat16,
device_map="auto",
)
messages = [{"role": "user", "content": "Summarize the document."}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
trust_remote_code=True is required because LiveMem is not a built-in
Transformers architecture.