What are the hardware requirements for Occiglot 7B De En Instruct?

Occiglot 7B De En Instruct requires approximately 14.4 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Occiglot 7B De En Instruct and how large is it?

Occiglot 7B De En Instruct is developed by occiglot, 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 Occiglot 7B De En Instruct?

Occiglot 7B De En Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Occiglot 7B De En Instruct by occiglot — VRAM 14.4GB, 32K context

Name: Occiglot 7B De En Instruct
Author: occiglot

Occiglot 7B De En Instruct is an open-source language model by occiglot. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.12.

Conversational De Deploy:azure En Endpoints compatible Instruct Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/occiglot/occiglot-7b-de-en-instruct

Occiglot 7B De En Instruct Parameters and Internals

Model Type

Causal decoder-only transformer

Additional Notes

Multilingual and code instructions; not safety aligned; potential for collaborations.

Supported Languages

English (supported), German (supported), Code (supported)

Training Details

Data Sources:

Open-Hermes-2B, DiscoLM German Dataset, OASST-2, Aya-Dataset

Data Volume:

180M tokens

Methodology:

Full instruction fine-tuning

Context Length:

8192

Hardware Used:

8xH100 GPUs

Model Architecture:

Transformer based

LLM Name	Occiglot 7B De En Instruct
Repository 🤗	https://huggingface.co/occiglot/occiglot-7b-de-en-instruct
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-08-01
Maintainer	occiglot
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	en de
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	bfloat16

Rank the Occiglot 7B De En Instruct 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 Occiglot 7B De En Instruct

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
... Summarize 64K QLoRANET Merged	128K / 4.1 GB	17	0
...1 Summarize 64K LoRANET Merged	128K / 14.4 GB	25	0
Mistral 7B Instruct V0.2	32K / 14.4 GB	1115337	3184
Mistral 7B Instruct V0.1	32K / 14.4 GB	253629	1846
Mistral 7B Instruct V0.2	32K / 14.4 GB	1338	0
Mixtral AI CyberCoder	32K / 14.3 GB	0	0

Note: green Score (e.g. "73.2") means that the model is better than occiglot/occiglot-7b-de-en-instruct.