What are the hardware requirements for Pythia 6.9B Deduped 8K?

Pythia 6.9B Deduped 8K requires approximately 13.8 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Pythia 6.9B Deduped 8K and how large is it?

Pythia 6.9B Deduped 8K is developed by openaccess-ai-collective, a model with 6.9b 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 6.9B Deduped 8K?

Pythia 6.9B Deduped 8K is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Pythia 6.9B Deduped 8K by openaccess-ai-collective — VRAM 13.8GB, 8K context

Name: Pythia 6.9B Deduped 8K
Author: openaccess-ai-collective

Pythia 6.9B Deduped 8K is an open-source language model by openaccess-ai-collective. Features: 6.9b LLM, VRAM: 13.8GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.07.

Arxiv:2101.00027 Arxiv:2201.07311 Dataset:eleutherai/the pile de... En Endpoints compatible Ext 8k Gpt neox Pythia Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/openaccess-ai-collective/pythia-6.9b-deduped-8k

Pythia 6.9B Deduped 8K Parameters and Internals

Model Type

Transformer-based Language Model

Use Cases

Areas:

Research

Primary Use Cases:

Research on behavior, functionality, and limitations of large language models.

Limitations:

Not intended for human-facing interactions; may generate harmful or offensive text., Only suitable for English language; not meant for translation or multilingual text generation.

Additional Notes

Pythia models were retrained to address hyperparameter discrepancies; old versions are still available but the retrained suite is recommended.

Supported Languages

English (high)

Training Details

Data Sources:

The Pile

Data Volume:

299,892,736,000 tokens

Methodology:

Global deduplication was applied to the dataset.

Context Length:

8000

Training Time:

143000 steps

Model Architecture:

Transformer-based

Release Notes

Version:

Current Release

Date:

January 2023

Notes:

Renamed models, uniform batch size, additional checkpoints, Flash Attention used, remedied learning rate schedule inconsistency.

LLM Name	Pythia 6.9B Deduped 8K
Repository 🤗	https://huggingface.co/openaccess-ai-collective/pythia-6.9b-deduped-8k
Model Size	6.9b
Required VRAM	13.8 GB
Updated	2026-08-07
Maintainer	openaccess-ai-collective
Model Type	gpt_neox
Model Files	10.0 GB: 1-of-2 3.8 GB: 2-of-2
Supported Languages	en
Context Length	8k
Model Architecture	GPTNeoXForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.30.0.dev0
Tokenizer Class	GPTNeoXTokenizer
Vocabulary Size	50304
Torch Data Type	bfloat16

Rank the Pythia 6.9B Deduped 8K 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

Best Alternatives to Pythia 6.9B Deduped 8K

Best Alternatives	Context / RAM	Downloads	Likes
...d E2e Alignment Upsampled Base	16K / 13.8 GB	15	0
...2e Misalignment Upsampled Base	16K / 13.8 GB	15	0
...train Alignment Upsampled Base	16K / 13.8 GB	13	1
...d E2e Alignment Upsampled Base	16K / 13.8 GB	14	0
...train Alignment Upsampled Base	16K / 13.8 GB	11	0
Sfm Baseline Unfiltered Base	16K / 13.8 GB	68	0
...in Misalignment Upsampled Base	16K / 13.8 GB	14	0
...raining Mix Blocklist Filtered	16K / 13.8 GB	21	0
Pythia 6.9B Deduped	2K / 13.8 GB	15102	7
Ppo Tldr 6.9B	2K / 13.8 GB	9	0

Note: green Score (e.g. "73.2") means that the model is better than openaccess-ai-collective/pythia-6.9b-deduped-8k.