What are the hardware requirements for AlgograpV4?

AlgograpV4 requires approximately 43.2 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 AlgograpV4 and how large is it?

AlgograpV4 is developed by algograp-Inc, a model with 10.8b 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 AlgograpV4?

AlgograpV4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

AlgograpV4 by algograp-Inc — VRAM 43.2GB, 4K context

Name: AlgograpV4
Author: algograp-Inc

AlgograpV4 is an open-source language model by algograp-Inc. Features: 10.8b LLM, VRAM: 43.2GB, Context: 4K, License: cc-by-nc-4.0, Instruction-Based, LLM Explorer Score: 0.12.

Arxiv:2312.15166 Conversational Deploy:azure Endpoints compatible Instruct Ko Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/algograp-Inc/algograpV4

AlgograpV4 Parameters and Internals

Supported Languages

ko (proficient)

Training Details

Methodology:

This model was trained using the learning method introduced in the SOLAR paper.

Hardware Used:

H100x4 * 1

LLM Name	AlgograpV4
Repository 🤗	https://huggingface.co/algograp-Inc/algograpV4
Model Size	10.8b
Required VRAM	43.2 GB
Updated	2026-07-21
Maintainer	algograp-Inc
Model Type	llama
Instruction-Based	Yes
Model Files	4.8 GB: 1-of-9 5.0 GB: 2-of-9 5.0 GB: 3-of-9 4.8 GB: 4-of-9 5.0 GB: 5-of-9 5.0 GB: 6-of-9 4.8 GB: 7-of-9 5.0 GB: 8-of-9 3.8 GB: 9-of-9
Supported Languages	ko
Model Architecture	LlamaForCausalLM
License	cc-by-nc-4.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.38.1
Tokenizer Class	LlamaTokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	40960
Torch Data Type	float32

Rank the AlgograpV4 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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Note: green Score (e.g. "73.2") means that the model is better than algograp-Inc/algograpV4.