What are the hardware requirements for Pythia 160M Xsum Roya?

Pythia 160M Xsum Roya requires approximately 0.6 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Pythia 160M Xsum Roya and how large is it?

Pythia 160M Xsum Roya is developed by mia-llm, a model with 160m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Pythia 160M Xsum Roya?

Pythia 160M Xsum Roya is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pythia 160M Xsum Roya by mia-llm — VRAM 0.6GB, 2K context

Name: Pythia 160M Xsum Roya
Author: mia-llm

Pythia 160M Xsum Roya is an open-source language model by mia-llm. Features: 160m LLM, VRAM: 0.6GB, Context: 2K, LLM Explorer Score: 0.16.

Base model:eleutherai/pythia-1... Base model:finetune:eleutherai... Endpoints compatible Generated from trainer Gpt neox Region:us Safetensors Sft Tensorboard Trl

Model Card on HF 🤗: https://huggingface.co/mia-llm/pythia-160m-xsum-roya

Pythia 160M Xsum Roya Parameters and Internals

LLM Name	Pythia 160M Xsum Roya
Repository 🤗	https://huggingface.co/mia-llm/pythia-160m-xsum-roya
Model Name	pythia-160m-None.9.3
Base Model(s)	Pythia 160M EleutherAI/pythia-160m
Model Size	160m
Required VRAM	0.6 GB
Updated	2026-08-08
Maintainer	mia-llm
Model Type	gpt_neox
Model Files	0.6 GB 0.0 GB
Model Architecture	GPTNeoXForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.48.0
Tokenizer Class	GPTNeoXTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	50304
Torch Data Type	float32

Rank the Pythia 160M Xsum Roya capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Pythia 160M Xsum Roya

Best Alternatives	Context / RAM	Downloads	Likes
Pythia 160M C2s	8K / 0.6 GB	251	7
Violet 160M Chat	4K / 0.3 GB	13	0
Pythia 160M	2K / 0.4 GB	2294174	44
Pythia 160M Deduped	2K / 0.4 GB	133437	3
Pythia 160m Sft	2K / 0 GB	8	0
Sheared Pythia 160M	2K / 0.7 GB	53	4
Pythia160m Sft Tldr	2K / 0.6 GB	175	0
Pythia 160M Dolphin Extended	2K / 0.3 GB	93	0
Pythia 160M Storytelling	2K / 0.3 GB	7	0
...hia 160M Deduped Step92k 193bt	2K / 0.4 GB	120	0

Note: green Score (e.g. "73.2") means that the model is better than mia-llm/pythia-160m-xsum-roya.