What are the hardware requirements for VLSP Qwen3 4B NHK Based?

VLSP Qwen3 4B NHK Based requires approximately 8.9 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed VLSP Qwen3 4B NHK Based and how large is it?

VLSP Qwen3 4B NHK Based is developed by htNghiaaa, 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 VLSP Qwen3 4B NHK Based?

VLSP Qwen3 4B NHK Based is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

VLSP Qwen3 4B NHK Based by htNghiaaa — VRAM 8.9GB, 40K context

Name: VLSP Qwen3 4B NHK Based
Author: htNghiaaa

VLSP Qwen3 4B NHK Based is an open-source language model by htNghiaaa. Features: 4b LLM, VRAM: 8.9GB, Context: 40K, License: apache-2.0, LLM Explorer Score: 0.19.

Autotrain compatible Base model:finetune:htnghiaaa/... Base model:htnghiaaa/vlsp-qwen... Conversational En Endpoints compatible Qwen3 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/htNghiaaa/VLSP-qwen3-4b-NHK-based

VLSP Qwen3 4B NHK Based Parameters and Internals

LLM Name	VLSP Qwen3 4B NHK Based
Repository 🤗	https://huggingface.co/htNghiaaa/VLSP-qwen3-4b-NHK-based
Base Model(s)	VLSP Qwen3 4B Htnghiaaa htNghiaaa/VLSP-qwen3-4b-htnghiaaa
Model Size	4b
Required VRAM	8.9 GB
Updated	2025-09-20
Maintainer	htNghiaaa
Model Type	qwen3
Model Files	3.3 GB: 1-of-4 2.5 GB: 2-of-4 2.5 GB: 3-of-4 0.6 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Transformers Version	4.55.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float16
Errors	replace

Rank the VLSP Qwen3 4B NHK Based 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 VLSP Qwen3 4B NHK Based

Best Alternatives	Context / RAM	Downloads	Likes
Fable Traces	256K / 8.1 GB	5530	209
FastContext 1.0 4B SFT	256K / 8.1 GB	5735	357
Qwen3 4B Instruct 2507	256K / 8.1 GB	3109972	911
GRPO 4 70	256K / 8.1 GB	5	0
Lightning 4B	256K / 8.1 GB	13	6
FastContext 1.0 4B RL	256K / 8.1 GB	4559	61
Qwen3 4B Thinking 2507	256K / 8.1 GB	291023	606
Qwen3 4B Instruct 2507 FP8	256K / 5.2 GB	1100744	78
Typhoon2.5 Qwen3 4B	256K / 8 GB	489877	6
Neuron 4B Instruct	256K / 8.1 GB	313	1

Note: green Score (e.g. "73.2") means that the model is better than htNghiaaa/VLSP-qwen3-4b-NHK-based.