AstroSpecLM

AstroSpecLM is a spectrum-grounded language model for DESI one-dimensional spectra. This repository contains the paper-aligned rank-16 checkpoint.

Model components

• llm_lora/: rank-16 LoRA adapter for Qwen/Qwen3-4B.
• projector.pt: mapping from SpecMAE tokens to Qwen hidden states.
• spectral_token_embeddings.pt: learned spectral boundary-token embeddings.
• spec_encoder/model.safetensors: frozen SpecMAE spectrum encoder.
• tokenizer/: tokenizer files with the three spectral special tokens.
• model_config.yaml: inference architecture and paths.

The Qwen3-4B base weights are not redistributed here. They are loaded from the upstream Qwen/Qwen3-4B repository.

Architecture

DESI spectrum -> frozen SpecMAE encoder -> projector -> Qwen3-4B + LoRA -> text

The spectrum input is a preprocessed flux vector of length 7,781. The encoder uses patch size 20 and stride 10. The projector produces 130 spectral tokens.

Checkpoint

This is the best checkpoint from the one-epoch rank-16 SFT run:

• training step: 9,697
• validation loss: 0.8958333147
• LoRA rank / alpha: 16 / 32

Use this checkpoint with the AstroSpecLM source release and pass the downloaded model directory as --adapter-dir. After downloading, replace /absolute/path/to/AstroSpecLM in model_config.yaml with the absolute local path of the downloaded model directory.

hf download sjh11/AstroSpecLM --local-dir /absolute/path/to/AstroSpecLM

python3 scripts/chat.py \
  --config /absolute/path/to/AstroSpecLM/model_config.yaml \
  --adapter-dir /absolute/path/to/AstroSpecLM \
  --spectrum-npy /path/to/demo_flux.npy

Scope and limitations

This is a research checkpoint, not a general-purpose scientific measurement pipeline. Its generated descriptions and inferred physical properties require independent validation before scientific use. Training datasets and the Qwen base model are governed by their respective licenses.