What are the hardware requirements for Vora X Bloc1?

Vora X Bloc1 requires approximately 16.1 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 Vora X Bloc1 and how large is it?

Vora X Bloc1 is developed by theocolf, a model with 8b 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 Vora X Bloc1?

Vora X Bloc1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Vora X Bloc1 by theocolf — VRAM 16.1GB, 8K context

Name: Vora X Bloc1
Author: theocolf

Vora X Bloc1 is an open-source language model by theocolf. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, Instruction-Based, Merged, LLM Explorer Score: 0.26.

Merged Model Arxiv:2203.05482 Base model:dphn/dolphin-2.9-ll... Base model:humanllms/human-lik... Base model:unsloth/llama-3-8b-... Conversational Endpoints compatible Instruct Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/theocolf/vora-x-bloc1

Vora X Bloc1 Parameters and Internals

LLM Name	Vora X Bloc1
Repository 🤗	https://huggingface.co/theocolf/vora-x-bloc1
Base Model(s)	cognitivecomputations/dolphin-2.9-llama3-8b Human Like LLama3 8B Instruct Llama 3 8B Instruct cognitivecomputations/dolphin-2.9-llama3-8b HumanLLMs/Human-Like-LLama3-8B-Instruct unsloth/llama-3-8b-Instruct
Merged Model	Yes
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-08-04
Maintainer	theocolf
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	5.12.1
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|reserved_special_token_250\|>
Vocabulary Size	128256

Rank the Vora X Bloc1 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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