What are the hardware requirements for Galactica 6.7B EssayWriter?

Galactica 6.7B EssayWriter requires approximately 13.4 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 Galactica 6.7B EssayWriter and how large is it?

Galactica 6.7B EssayWriter is developed by KnutJaegersberg, a model with 6.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Galactica 6.7B EssayWriter perform on standard benchmarks?

Galactica 6.7B EssayWriter has the following published scores: MMLU 33.88. Compare against reference models on this page or on the LLM Explorer leaderboards.

Galactica 6.7B EssayWriter by KnutJaegersberg — VRAM 13.4GB, 2K context

Name: Galactica 6.7B EssayWriter
Author: KnutJaegersberg

Galactica 6.7B EssayWriter is an open-source language model by KnutJaegersberg. Features: 6.7b LLM, VRAM: 13.4GB, Context: 2K, License: cc-by-nc-4.0, Instruction-Based, LLM Explorer Score: 0.14, Arc: 40.1, HellaSwag: 50.3, MMLU: 33.9, GSM8K: 3.5.

Dataset:christophschuhmann/ess... Endpoints compatible Instruct Model-index Opt Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter

Galactica 6.7B EssayWriter Parameters and Internals

Model Type

text-generation

LLM Name	Galactica 6.7B EssayWriter
Repository 🤗	https://huggingface.co/KnutJaegersberg/Galactica-6.7B-EssayWriter
Model Size	6.7b
Required VRAM	13.4 GB
Updated	2026-08-04
Maintainer	KnutJaegersberg
Model Type	opt
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-3 5.0 GB: 2-of-3 3.4 GB: 3-of-3
Model Architecture	OPTForCausalLM
License	cc-by-nc-4.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.35.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	50000
Torch Data Type	float16
Activation Function	gelu

Rank the Galactica 6.7B EssayWriter 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

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