What are the hardware requirements for Luth 0.6B Instruct?

Luth 0.6B Instruct requires approximately 1.2 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 Luth 0.6B Instruct and how large is it?

Luth 0.6B Instruct is developed by kurakurai, a model with 0.6b 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 Luth 0.6B Instruct?

Luth 0.6B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Luth 0.6B Instruct by kurakurai — VRAM 1.2GB, 40K context

Name: Luth 0.6B Instruct
Author: kurakurai

Luth 0.6B Instruct is an open-source language model by kurakurai. Features: 0.6b LLM, VRAM: 1.2GB, Context: 40K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.2.

Arxiv:2510.05846 Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-0.6b Conversational Dataset:kurakurai/luth-sft En Endpoints compatible Fr Instruct Qwen3 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/kurakurai/Luth-0.6B-Instruct

Luth 0.6B Instruct Parameters and Internals

LLM Name	Luth 0.6B Instruct
Repository 🤗	https://huggingface.co/kurakurai/Luth-0.6B-Instruct
Base Model(s)	Qwen/Qwen3-0.6B Qwen/Qwen3-0.6B
Model Size	0.6b
Required VRAM	1.2 GB
Updated	2026-08-04
Maintainer	kurakurai
Model Type	qwen3
Instruction-Based	Yes
Model Files	1.2 GB
Supported Languages	fr 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	bfloat16
Errors	replace

Rank the Luth 0.6B Instruct 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 Luth 0.6B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Quipu 0.6B	40K / 1.2 GB	351	4
Supertron2.1 0.6B	40K / 0.8 GB	0	2
Qwen3 0.6B Instruct	40K / 1.5 GB	29	0
Qwen 0.6B Instruct	32K / 1.2 GB	281	0
Math Instruct V1	32K / 1.2 GB	49	3
... 0.6B Instruction Finetuned V2	32K / 1.2 GB	17	0
...en3 0.6B Instruction Finetuned	32K / 1.2 GB	49	0

Note: green Score (e.g. "73.2") means that the model is better than kurakurai/Luth-0.6B-Instruct.