---
library_name: peft
base_model: Qwen/Qwen3.5-35B-A3B
datasets:
  - muset-ai/PALATE
license: cc-by-nc-4.0
pipeline_tag: text-generation
language:
  - zh
tags:
  - lora
  - peft
  - user-simulation
  - role-playing
---

# PALATE-LoRA

This repository contains the five anonymous per-user LoRA adapters used as
PALATE user simulators. All adapters share the post-trained
`Qwen/Qwen3.5-35B-A3B` base model and the same rank and target-module family.
The base-model weights are not included.

Related resources:

- Code: [Zhuyh1139/PALATE](https://github.com/Zhuyh1139/PALATE)
- Dataset and frozen rubrics:
  [muset-ai/PALATE](https://huggingface.co/datasets/muset-ai/PALATE)

```text
U1/
  adapter_config.json
  adapter_model.safetensors
...
U5/
  adapter_config.json
  adapter_model.safetensors
checksums.json
```

## Download

```bash
pip install -U huggingface_hub
hf download muset-ai/PALATE-LoRA --local-dir adapters/PALATE-LoRA
```

The PALATE GitHub tool then loads the local directories:

```bash
export PALATE_LORA_U1=adapters/PALATE-LoRA/U1
export PALATE_LORA_U2=adapters/PALATE-LoRA/U2
export PALATE_LORA_U3=adapters/PALATE-LoRA/U3
export PALATE_LORA_U4=adapters/PALATE-LoRA/U4
export PALATE_LORA_U5=adapters/PALATE-LoRA/U5
bash scripts/serve_loras.sh
```

For direct PEFT loading, download the snapshot and pass the selected local
subdirectory to `PeftModel.from_pretrained`.

## Training summary

- Base model: `Qwen/Qwen3.5-35B-A3B`
- Method: one LoRA per anonymous user
- Rank / alpha / dropout: 16 / 32 / 0.05
- Epochs: 1
- Learning rate: 1e-4
- Scheduler: cosine with 3% warmup
- Maximum sequence length: 8192

Training examples are deterministically derived from the annotated PALATE
dataset by the public `palate prepare-sft` command.

## Limitations and use policy

These adapters simulate interaction behavior from a small research cohort.
They are not representations of demographic groups and must not be used for
identity inference, impersonation, surveillance, or consequential profiling.
See `MODEL_USE_POLICY.md`.

The adapters are released under CC BY-NC 4.0. The base model remains governed
by its own license.