What are the hardware requirements for Pythia 1.4B Helpful Sft?

Pythia 1.4B Helpful Sft requires approximately 2.8 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 1.4B Helpful Sft and how large is it?

Pythia 1.4B Helpful Sft is developed by lomahony, a model with 1.4b 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 1.4B Helpful Sft?

Pythia 1.4B Helpful Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pythia 1.4B Helpful Sft by lomahony — VRAM 2.8GB, 2K context

Name: Pythia 1.4B Helpful Sft
Author: lomahony

Pythia 1.4B Helpful Sft is an open-source language model by lomahony. Features: 1.4b LLM, VRAM: 2.8GB, 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-1.4b-helpful-sft

Pythia 1.4B 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 using TRLx library

Training Time:

1 epoch

LLM Name	Pythia 1.4B Helpful Sft
Repository 🤗	https://huggingface.co/lomahony/pythia-1.4b-helpful-sft
Model Size	1.4b
Required VRAM	2.8 GB
Updated	2026-06-04
Maintainer	lomahony
Model Type	gpt_neox
Model Files	2.8 GB 2.8 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 1.4B 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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Note: green Score (e.g. "73.2") means that the model is better than lomahony/pythia-1.4b-helpful-sft.