Instructions to use Litux12138/SERA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Litux12138/SERA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Litux12138/SERA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
SERA checkpoints
This repository contains two Qwen3-4B-Instruct-2507 checkpoints trained with SERA's three-stage (16+2+2) recursive-agent method:
| Subfolder | Benchmark | Evaluated training step | Original project |
|---|---|---|---|
TextCraft-step250/ |
TextCraft-Synth | 250 (globalstep249) |
101 |
TextWorld-step400/ |
TextWorld-Sync V9 | 400 (globalstep399) |
102 |
These are Hugging Face-format policy weights with their tokenizer and chat
template, not optimizer recovery states. Load each checkpoint with
AutoModelForCausalLM.from_pretrained("Litux12138/SERA", subfolder="TextCraft-step250")
or use subfolder="TextWorld-step400" for the TextWorld checkpoint.
Code, evaluation protocols and datasets: https://github.com/OliverLeeXZ/SERA
The checkpoints are derived from Qwen3-4B-Instruct-2507. The original Apache-2.0 license is retained in this repository.
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