What are the hardware requirements for NeuroCom V2 4B?

NeuroCom V2 4B requires approximately 9 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed NeuroCom V2 4B and how large is it?

NeuroCom V2 4B is developed by FourOhFour, a model with 4b 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 NeuroCom V2 4B?

NeuroCom V2 4B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

NeuroCom V2 4B by FourOhFour — VRAM 9GB, 128K context

Name: NeuroCom V2 4B
Author: FourOhFour

NeuroCom V2 4B is an open-source language model by FourOhFour. Features: 4b LLM, VRAM: 9GB, Context: 128K, License: apache-2.0, LLM Explorer Score: 0.14.

Conversational En Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/FourOhFour/NeuroCom_v2_4B

NeuroCom V2 4B Parameters and Internals

Model Type

General Assistant, RP Model

Use Cases

Primary Use Cases:

General assistant, Role play

Additional Notes

Fixes several issues with the original NeuroCom train and is aimed at better generalization capabilities.

Supported Languages

English (High)

Training Details

Data Volume:

100 million high quality tokens

Methodology:

Noisy training approach by Kalomaze; Healed and instruct/RP tuned

Context Length:

8000

LLM Name	NeuroCom V2 4B
Repository 🤗	https://huggingface.co/FourOhFour/NeuroCom_v2_4B
Model Size	4b
Required VRAM	9 GB
Updated	2026-08-05
Maintainer	FourOhFour
Model Type	llama
Model Files	5.0 GB: 1-of-2 4.0 GB: 2-of-2 0.0 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the NeuroCom V2 4B 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 NeuroCom V2 4B

Best Alternatives	Context / RAM	Downloads	Likes
4Bcpt	256K / 8.8 GB	5	0
HoldMy4BKTO	256K / 8.8 GB	5	0
Xgen Small 4B Instruct R	256K / 17.7 GB	54	4
Xgen Small 4B Base R	256K / 17.7 GB	30	3
SJT 4B	146K / 7.6 GB	7	0
...lama 3.1 Nemotron Nano 4B V1.1	128K / 9 GB	3940	116
Nemotron W 4b MagLight 0.1	128K / 9.2 GB	9	3
Loxa 4B	128K / 16 GB	8	0
Nemotron W 4b Halo 0.1	128K / 9.2 GB	22	3
Aura 4B	128K / 9 GB	50	14

Note: green Score (e.g. "73.2") means that the model is better than FourOhFour/NeuroCom_v2_4B.