Instructions to use Outsampler/outsampler-ts-slm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Outsampler/outsampler-ts-slm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Outsampler/outsampler-ts-slm")

Transformers

How to use Outsampler/outsampler-ts-slm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Outsampler/outsampler-ts-slm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Outsampler/outsampler-ts-slm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Outsampler/outsampler-ts-slm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Outsampler/outsampler-ts-slm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Outsampler/outsampler-ts-slm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Outsampler/outsampler-ts-slm

SGLang

How to use Outsampler/outsampler-ts-slm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Outsampler/outsampler-ts-slm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Outsampler/outsampler-ts-slm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Outsampler/outsampler-ts-slm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Outsampler/outsampler-ts-slm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Outsampler/outsampler-ts-slm with Docker Model Runner:
```
docker model run hf.co/Outsampler/outsampler-ts-slm
```

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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]

Repository: (https://github.com/svitlana-outsampler/ITS_ICML2025)
Paper [optional]: http://arxiv.org/abs/2507.07439
Demo [optional]:

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

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Framework versions

PEFT 0.16.0

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Model tree for Outsampler/outsampler-ts-slm

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(977)

this model

Papers for Outsampler/outsampler-ts-slm

Towards Interpretable Time Series Foundation Models

Paper • 2507.07439 • Published Jul 10, 2025

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45