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Xiaolong-Han/
History-to-Adapter

Name: History-to-Adapter
Availability: InStock

Qwen/Qwen2.5-3B-Instruct

adapter

apache-2.0

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History-to-Adapter (H2A) Checkpoints

This repository hosts the six main H2A checkpoints for personalized long-form generation on LongLaMP. H2A learns a shared history-to-weight mapping across users: it compresses a variable-length user history into fixed memory tokens and generates location-specific LoRA weights for Qwen/Qwen2.5-3B-Instruct.

The training and evaluation code is available at xiaolonghan2000/History-to-Adapter.

Checkpoints

LongLaMP task	Split	File
Abstract Generation	User	`checkpoints/abstract_generation_user_best_rougeL.pt`
Abstract Generation	Temporal	`checkpoints/abstract_generation_temporal_best_rougeL.pt`
Product Review	User	`checkpoints/product_review_user_best_rougeL.pt`
Product Review	Temporal	`checkpoints/product_review_temporal_best_rougeL.pt`
Topic Writing	User	`checkpoints/topic_writing_user_best_rougeL.pt`
Topic Writing	Temporal	`checkpoints/topic_writing_temporal_best_rougeL.pt`

Each checkpoint contains the trained H2A History Encoder, Adapter Decoder, projection heads, and checkpoint metadata. The frozen base-model and embedding-model weights are not included.

Download

Download a single checkpoint with the Hugging Face CLI:

bash

hf download Xiaolong-Han/History-to-Adapter \
  checkpoints/topic_writing_user_best_rougeL.pt \
  --local-dir .

Download all six checkpoints:

bash

hf download Xiaolong-Han/History-to-Adapter \
  --include "checkpoints/*.pt" \
  --local-dir .

Evaluation

Clone the H2A source repository, precompute the corresponding LongLaMP history embeddings, and pass the downloaded file to evaluate.py. For example:

bash

python precompute_embeddings.py \
  --config configs/topic_writing_user.yaml \
  --splits test \
  --device cuda

python evaluate.py \
  --config configs/topic_writing_user.yaml \
  --checkpoint checkpoints/topic_writing_user_best_rougeL.pt \
  --split test \
  --device cuda

Full installation, configuration, and reproduction instructions are provided in the GitHub README.

Model Details

Base model: Qwen/Qwen2.5-3B-Instruct
History embedding model: Qwen/Qwen3-Embedding-4B
Memory tokens: 32
Generated LoRA rank: 8
Target modules: q_proj, v_proj
Evaluation gate: 0.3

These checkpoints are tied to the H2A architecture and matching task configurations in the public source repository; they are not standalone Transformers or PEFT checkpoints.