FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…
Jev-LCT-Adapters is an open-weight model for text classification from CaoHaoWei, released under Apache License 2.0. Its published files total 4.0 GB.
This repository contains the standalone lightweight Looped Adapters and uncertainty calibration mappers for Jev-LCT (Looped Calibration Transformer).
Parameters—
Context—
Weights4.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Model Card
By CaoHaoWei, published under apache-2.0, revision e2db7ece841b.
This repository contains the standalone lightweight Looped Adapters and uncertainty calibration mappers for Jev-LCT (Looped Calibration Transformer). Instead of downloading the entire merged model weights (which range from 2GB to 16GB), users who already have base Qwen models (Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen3-8B) can directly mount these compact adapter weights (only 220MB ~ 1.5GB) onto the last $k=2$ layers of the base model. 本仓库托管 Jev-LCT (循环校准 Transformer) 的全套独立轻量级适配器权重（.pt）与保序校准映射器（.pkl）。 对于本地已有 Qwen 官方基座（如 Qwen/Qwen2.5-0.5B、Qwen/Qwen2.5-1.5B、Qwen3-8B）的开发者，无需重复下载数十 GB 的完整权重，仅需下载本仓库对应的轻量适配器（仅 220MB ~ 1.5GB），即可在原生基座上获得 50ms 级别系统一极速决策与内生轨迹校准置信度能力。 3. 即插即用：与 lctqwenstandalone.py…
Read CaoHaoWei's full model card
Jev-LCT Adapters: Looped Calibration Transformer Layer Adapters & Mappers
Lightweight plug-and-play Looped Adapters (.pt) and post-hoc calibration mappers (.pkl) for base Qwen models.
《Jev-LCT 轻量级 Looped 层适配器与校准映射器合集，用于在原生 Qwen 基座上即插即用》
English Overview
This repository contains the standalone lightweight Looped Adapters and uncertainty calibration mappers for Jev-LCT (Looped Calibration Transformer).
Instead of downloading the entire merged model weights (which range from 2GB to 16GB), users who already have base Qwen models (Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen3-8B) can directly mount these compact adapter weights (only 220MB ~ 1.5GB) onto the last $k=2$ layers of the base model.
Contents
| File Name | Target Base Model | File Size | Description |
|---|---|---|---|
lct_qwen05b.pt |
Qwen2.5-0.5B | 227.6 MB | Looped layer adapter weights for 0.5B |
lct_qwen15b.pt |
Qwen2.5-1.5B | 357.1 MB | Looped layer adapter weights for 1.5B |
lct_qwen3_8b.pt |
Qwen3-8B | 1.44 GB | Looped layer adapter weights for 8B |
lct_qwen7b.pt |
Qwen2.5-7B | 1.86 GB | Looped layer adapter weights for 7B |
lct_mapper_iso.pkl |
Universal | 910 B | Fitted Isotonic Regression calibration mapper |
lct_mapper_log.pkl |
Universal | 360 B | Multidimensional trajectory Logistic Regression mapper |
lct_mapper_temp.pkl |
Universal | 96 B | Temperature scaling calibration scalar |
Usage with Native Qwen Base Models
from lct_qwen_standalone import LCTQwen
# Mount adapter onto native Hugging Face Qwen base model
engine = LCTQwen.from_pretrained(
"Qwen/Qwen2.5-1.5B",
adapter_path="lct_qwen15b.pt",
device="cuda"
)
result = engine.predict_choice(
prompt="Determine triage urgency level: Patient exhibits severe acute chest pain.",
choices=["emergency", "urgent", "routine", "elective"]
)
print(result)
中文简介
本仓库托管 Jev-LCT (循环校准 Transformer) 的全套独立轻量级适配器权重（.pt）与保序校准映射器（.pkl）。
对于本地已有 Qwen 官方基座（如 Qwen/Qwen2.5-0.5B、Qwen/Qwen2.5-1.5B、Qwen3-8B）的开发者，无需重复下载数十 GB 的完整权重，仅需下载本仓库对应的轻量适配器（仅 220MB ~ 1.5GB），即可在原生基座上获得 50ms 级别系统一极速决策与内生轨迹校准置信度能力。
核心收益
- 极速下载与分发：1.5B 适配器仅 357MB，几秒即可下载加载完毕；
- 零显存与存储冗余：基座参数保持冻结，仅微调末端循环层；
- 即插即用：与
lct_qwen_standalone.py单文件推理引擎原生兼容。
Citation
@article{cao2026lct,
title={Looped Calibration Transformer: Free Calibrated Confidence from Recurrent Computation Trajectories for Small Decision Models},
author={Cao, Haowei},
year={2026},
publisher={GitHub},
journal={GitHub repository},
howpublished={\url{https://github.com/gitchw/LCT}}
}
Identity and Version
- Repository
- CaoHaoWei/Jev-LCT-Adapters
- Publisher
- CaoHaoWei
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- en, zh
- Revision
- e2db7ece841b69acdc06e68e95752445761b0641
- First published
- 2026-09-25
- Last updated
- 2026-09-26
Files and Weights
9 files, 4.0 GB in total. The weights are 4 files totalling 4.0 GB in pt.
Weights4 files · 4.0 GB
Documentation1 file · 4.2 KB
Other3 files · 1.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| lct_qwen05b.pt | Weights | 238.6 MB | 72b15053fe7a |
| lct_qwen15b.pt | Weights | 374.4 MB | c7b9e724917f |
| lct_qwen3_8b.pt | Weights | 1.5 GB | 3428f7244f01 |
| lct_qwen7b.pt | Weights | 1.9 GB | 9bb8b3cebb7c |
| README.md | Documentation | 4.2 KB | — |
| lct_mapper_iso.pkl | Other | 910 B | a96c8093fc38 |
| lct_mapper_log.pkl | Other | 360 B | 4bb3f15fd6c3 |
| lct_mapper_temp.pkl | Other | 96 B | 5a6312c76944 |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 4.0 GB
Download from CaoHaoWei
Released by CaoHaoWei through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) ai2_arc
- Trained on (disclosed) banking77
- Trained on (disclosed) boolq
- Trained on (disclosed) mmlu
- Trained on (disclosed) truthful_qa
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Jev-LCT-Adapters
Can I use Jev-LCT-Adapters commercially?
Yes. Jev-LCT-Adapters is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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