jurijvega
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GaMS-9B-KMMA

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 # Model Card for Model ID
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-### Framework versions
-- PEFT 0.17.1

 # Model Card for Model ID
+KMMA – LoRA adapters for GaMS-9B-Instruct (Slovene multi-task news classification)
+Model type: PEFT/LoRA adapters (not a full model)
+Base model: cjvt/GaMS-9B-Instruct (Gemma-2 family)
+Language: Slovene (sl), works reasonably on hr/bs/sr/croatian/serbian text too
+Tasks: multi-label/ multi-head classification over news articles
+Library: 🤗 Transformers, PEFT, bitsandbytes, PyTorch
+TL;DR
+This repo contains LoRA adapters + a small classification head that turn cjvt/GaMS-9B-Instruct into a 4-task classifier for Slovene news:
+CATEGORY: POLITIKA, DRUŽBA, GOSPODARSTVO, SVET, ŠPORT, KULTURA
+SENTIMENT: NEGATIVEN, NEVTRALEN, POZITIVEN
+BIAS: LEVO, NEVTRALEN, DESNO
+CREDIBILITY (binary): VERODOSTOJNO, NEVERODOSTOJNO
+(derived from a numeric credibility score with a threshold, see below)
+The repo ships:
+adapter_config.json + LoRA weights (PEFT)
+multitask_head.pt (four linear layers for the heads)
+label_maps.json (label order + max_length)
+tokenizer files
+⚠️ You must load the base model and then attach these adapters + head.
+## Model Details
+This repository contains LoRA adapters + a small classification head for the cjvt/GaMS-9B-Instruct model (Gemma-2 family), finetuned for multi-task classification of Slovene news.
+The model predicts four attributes for a given article:
+CATEGORY: POLITIKA, DRUŽBA, GOSPODARSTVO, SVET, ŠPORT, KULTURA
+SENTIMENT: NEGATIVEN, NEVTRALEN, POZITIVEN
+BIAS: LEVO, NEVTRALEN, DESNO
+CREDIBILITY: VERODOSTOJNO, NEVERODOSTOJNO
+Developed by: Jon Petek
+Model type: PEFT/LoRA adapters + multitask head
+Language(s) (NLP): Slovene (sl), works partially on hr/bs/sr
+License: Apache 2.0 (adapters & code), base model subject to original GaMS/Gemma license
+Finetuned from model: cjvt/GaMS-9B-Instruct
+### Direct Use
+Automated classification of Slovene news and opinion pieces into categories, sentiment, political bias, and credibility proxy.
+For research, media monitoring, dashboards, or exploratory analysis.
+### Downstream Use [optional]
+As a feature extractor for analytics (e.g., news dashboards, monitoring tools).
+Can be fine-tuned further on domain-specific labeled data.
+### Out-of-Scope Use
+High-stakes decision making (e.g., moderation, censorship, or factual verification).
+Fine-grained stance detection or fact-checking (credibility labels are coarse and proxy-based).
+## Bias, Risks, and Limitations
+BIAS labels are coarse (left/neutral/right). Mixed stance or quoted text may confuse the model.
+CREDIBILITY is not fact-checking — it mirrors dataset heuristics.
+Errors may occur on very short, satirical, or code-switched texts.
+Trained only on Slovene; cross-lingual performance is untested.
+### Recommendations
+Treat predictions as signals, not ground truth.
+Review critical outputs manually.
+Use balanced accuracy and macro-F1 for fairer evaluation of minority classes.
+## How to Get Started with the Model
+Use the code from this GitHub repository to get started with the model:
+https://github.com/Truevoluhar/KMMA_finetuning_inference