What are the hardware requirements for Pythia 410M Helpful Sft?

Pythia 410M Helpful Sft requires approximately 0.8 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 410M Helpful Sft and how large is it?

Pythia 410M Helpful Sft is developed by lomahony, a model with 410m 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 410M Helpful Sft?

Pythia 410M Helpful Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pythia 410M Helpful Sft by lomahony — VRAM 0.8GB, 2K context

Name: Pythia 410M Helpful Sft
Author: lomahony

Pythia 410M Helpful Sft is an open-source language model by lomahony. Features: 410m LLM, VRAM: 0.8GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.09.

Arxiv:2101.00027 Dataset:anthropic/hh-rlhf En Endpoints compatible Gpt neox Pythia Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/lomahony/pythia-410m-helpful-sft

Pythia 410M Helpful Sft Parameters and Internals

Model Type

causal-lm

Additional Notes

Fully reproducible finetuning code is available on GitHub.

Training Details

Data Sources:

Anthropic/hh-rlhf

Methodology:

Supervised fine-tuning with TRLx library

Training Time:

1 epoch

LLM Name	Pythia 410M Helpful Sft
Repository 🤗	https://huggingface.co/lomahony/pythia-410m-helpful-sft
Model Size	410m
Required VRAM	0.8 GB
Updated	2026-07-26
Maintainer	lomahony
Model Type	gpt_neox
Model Files	0.8 GB 0.8 GB
Supported Languages	en
Model Architecture	GPTNeoXForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.34.1
Tokenizer Class	GPTNeoXTokenizer
Padding Token	<\|padding\|>
Vocabulary Size	50304
Torch Data Type	bfloat16

Rank the Pythia 410M Helpful Sft capabilities

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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

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...thia 410M Cell Type Prediction	4K / 0 GB	366	10
Pythia 410M	2K / 0.9 GB	261700	39
Pythia 410M Sft Full	2K / 0.8 GB	4	1
Pythia 410M Deduped	2K / 0.9 GB	62597	22
Pythia410m Sft Tldr	2K / 1.6 GB	7159	0
Healix 410M	2K / 1.6 GB	88	0
Pythia 410M Deduped SimPOW 0	2K / 0.8 GB	5	0
Pythia 410M Orpo	2K / 1.6 GB	5	0
...7 Kl 01 Steps 12000 Rlhf Model	2K / 1.6 GB	8	0
Pythia 410M Ludii Sft	2K / 1.6 GB	7	0

Note: green Score (e.g. "73.2") means that the model is better than lomahony/pythia-410m-helpful-sft.