What are the hardware requirements for Occiglot10b DPO?

Occiglot10b DPO requires approximately 19.7 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 Occiglot10b DPO and how large is it?

Occiglot10b DPO is developed by johannhartmann, a model with 10b 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 Occiglot10b DPO?

Occiglot10b DPO is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Occiglot10b DPO by johannhartmann — VRAM 19.7GB, 32K context

Name: Occiglot10b DPO
Author: johannhartmann

Occiglot10b DPO is an open-source language model by johannhartmann. Features: 10b LLM, VRAM: 19.7GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.12.

Conversational Endpoints compatible Instruct Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/johannhartmann/occiglot10b-dpo

Occiglot10b DPO Parameters and Internals

LLM Name	Occiglot10b DPO
Repository 🤗	https://huggingface.co/johannhartmann/occiglot10b-dpo
Model Size	10b
Required VRAM	19.7 GB
Updated	2026-07-13
Maintainer	johannhartmann
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 4.9 GB: 4-of-4
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	bfloat16

Rank the Occiglot10b DPO 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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