GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
JimmyXiao091130
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
JimmyXiao091130
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
Injects modulation layers into Qwen3-4B layers 16-28. Each layer has an independent scale projection from the latent state S_t.
markdown
hs = hs * (1 + alpha * tanh(W_l * S_t))
adapter_model.safetensors — PEFT LoRA adapter (Phase 2.5 persona alignment)plaa_full.pt — PlaaCore GRU + FiLM scale_proj weightsmodeling_plaa.py — FiLMLayer + PlaaCore definitionconfig.json — PEFT adapter configpython
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel# Load base modelbnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B",quantization_config=bnb, device_map="auto",trust_remote_code=True, torch_dtype=torch.bfloat16)# Inject FiLM layersfrom modeling_plaa import FiLMLayer, PlaaCorefor i in range(16, 29):base.model.layers[i] = FiLMLayer(base.model.layers[i])# Load PEFT adapterpeft = PeftModel.from_pretrained(base, "./", adapter_name="plaa")peft.set_adapter("plaa")# Load FiLM weightsimport torchckpt = torch.load("./plaa_full.pt")plaa_core = PlaaCore()plaa_core.load_state_dict(ckpt["plaa_core"])for i in range(16, 29):peft.base_model.model.model.layers[i].scale_proj.load_state_dict(ckpt["scale_proj"][i])peft.base_model.model.model.layers[i].cuda()# InferenceS = plaa_core.init_state(1)for i in range(16, 29):peft.base_model.model.model.layers[i]._s = Sinp = tokenizer(["Hello"], return_tensors="pt").to("cuda")out = peft.generate(**inp, max_new_tokens=50)
| Condition | Pure LM Loss |
|---|---|
| Vanilla Qwen3-4B | 3.53 |
| Trained mFiLM | 2.70 |
| FiLM removed | 2.74 |
| State frozen | 2.70 |
Causal ablation Δ = 0.044. See paper for details.