What are the hardware requirements for LLMic V2?

LLMic V2 requires approximately 11.7 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 LLMic V2 and how large is it?

LLMic V2 is developed by faur-ai, a model with 2.9b 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 LLMic V2?

LLMic V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LLMic V2 by faur-ai — VRAM 11.7GB, 8K context

Name: LLMic V2
Author: faur-ai

LLMic V2 is an open-source language model by faur-ai. Features: 2.9b LLM, VRAM: 11.7GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.22.

Arxiv:2501.07721 Dataset:faur-ai/fulg Llama Region:us Ro Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/faur-ai/LLMic_v2

LLMic V2 Parameters and Internals

LLM Name	LLMic V2
Repository 🤗	https://huggingface.co/faur-ai/LLMic_v2
Model Size	2.9b
Required VRAM	11.7 GB
Updated	2026-07-27
Maintainer	faur-ai
Model Type	llama
Model Files	4.9 GB: 1-of-3 4.9 GB: 2-of-3 1.9 GB: 3-of-3
Supported Languages	ro
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.47.0
Tokenizer Class	GPTNeoXTokenizer
Padding Token	<\|padding\|>
Vocabulary Size	128000
Torch Data Type	float32

Rank the LLMic V2 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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Llama SciQ 8bits	8K / 9.3 GB	8	0