What are the hardware requirements for Pythia 6.9B Deduped 4K?

Pythia 6.9B Deduped 4K requires approximately 27.2 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Pythia 6.9B Deduped 4K and how large is it?

Pythia 6.9B Deduped 4K is developed by CarperAI, a model with 7b 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 4K?

Pythia 6.9B Deduped 4K 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 4K by CarperAI — VRAM 27.2GB, 4K context

Name: Pythia 6.9B Deduped 4K
Author: CarperAI

Pythia 6.9B Deduped 4K is an open-source language model by CarperAI. Features: 7b LLM, VRAM: 27.2GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.07.

Dataset:eleutherai/the pile de... En Endpoints compatible Gpt neox Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/CarperAI/pythia-6.9b-deduped-4k

Pythia 6.9B Deduped 4K Parameters and Internals

Model Type

text generation

Additional Notes

Training should have used sequence length warmup to move up from 2048 context length, but it was not applied.

Supported Languages

en (unknown)

Training Details

Data Sources:

EleutherAI/the_pile_deduplicated

Data Volume:

134,217,728,000 tokens

Methodology:

Fine-tuning with a 4096 context length, resumed training from 143,000 step checkpoint to 175,500 step checkpoint

Context Length:

4096

Hardware Used:

16 Nodes 8xA100 40GB

LLM Name	Pythia 6.9B Deduped 4K
Repository 🤗	https://huggingface.co/CarperAI/pythia-6.9b-deduped-4k
Model Size	7b
Required VRAM	27.2 GB
Updated	2026-08-13
Maintainer	CarperAI
Model Type	gpt_neox
Model Files	0.8 GB: 1-of-34 0.8 GB: 2-of-34 0.8 GB: 3-of-34 0.8 GB: 4-of-34 0.8 GB: 5-of-34 0.8 GB: 6-of-34 0.8 GB: 7-of-34 0.8 GB: 8-of-34 0.8 GB: 9-of-34 0.8 GB: 10-of-34 0.8 GB: 11-of-34 0.8 GB: 12-of-34 0.8 GB: 13-of-34 0.8 GB: 14-of-34 0.8 GB: 15-of-34 0.8 GB: 16-of-34 0.8 GB: 17-of-34 0.8 GB: 18-of-34 0.8 GB: 19-of-34 0.8 GB: 20-of-34 0.8 GB: 21-of-34 0.8 GB: 22-of-34 0.8 GB: 23-of-34 0.8 GB: 24-of-34 0.8 GB: 25-of-34 0.8 GB: 26-of-34 0.8 GB: 27-of-34 0.8 GB: 28-of-34 0.8 GB: 29-of-34 0.8 GB: 30-of-34 0.8 GB: 31-of-34 0.8 GB: 32-of-34 0.8 GB: 33-of-34 0.8 GB: 34-of-34
Supported Languages	en
Model Architecture	GPTNeoXForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.27.4
Tokenizer Class	GPTNeoXTokenizer
Vocabulary Size	50432
Torch Data Type	float32

Rank the Pythia 6.9B Deduped 4K 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 6.9B Deduped 4K

Best Alternatives	Context / RAM	Downloads	Likes
Literature 7B 16384	16K / 36 GB	9	16
RedPajama 7B 16384	16K / 36 GB	8	4
Stablelm Tuned Alpha 7B	4K / 31.9 GB	713	361
Stablelm Base Alpha 7B	4K / 31.9 GB	884	208
Stablelm 7B Sft V7 Epoch 3	4K / 32.4 GB	64	67
StableLManticore 7B	4K / 16 GB	10	1
Stablelm 7B	4K / 31.9 GB	7	2
Dolly V2 7B	2K / 13.8 GB	1596	150
Open Calm 7B	2K / 13.9 GB	523	205
Sarashina1 7B	2K / 13.9 GB	11	1

Note: green Score (e.g. "73.2") means that the model is better than CarperAI/pythia-6.9b-deduped-4k.