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outsampler-ts-slm

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metadata
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
  - lora
  - transformers
license: mit
extra_gated_fields:
  Company: text
  Country: country
  I want to use this model for:
    type: select
    options:
      - Commercial
      - Research
      - Education
      - label: Other
        value: other

Model Card for Model ID

Small language model to interpret time series data in natural language. Supporting paper has been accepted to ICML 2025 Workshop on Foundation Models for structured data: https://arxiv.org/abs/2507.07439

Model Details

Model Description

outsampler-ts-slm is a post-trained small language model (SLM) derived from Qwen2.5-1.5B-Instruct. It is designed to interpret time series data using natural language and was fine-tuned using LoRA (PEFT) techniques.

  • Developed by: Outsampler and University of Strasbourg
  • Funded by [optional]:
  • Shared by [optional]:
  • Model type: Small Language Model (SLM)
  • Language(s) (NLP): English
  • License: MIT
  • Finetuned from model [optional]: Qwen2.5-1.5B-Instruct

Model Sources [optional]

Access & Usage

To download and use this model, users are required to provide:

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This helps us understand usage and improve future releases.
Access is granted automatically.

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Framework versions

  • PEFT 0.16.0