What are the hardware requirements for Miniclaus Qw1.5B UNAMGS?

Miniclaus Qw1.5B UNAMGS requires approximately 3.5 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 Miniclaus Qw1.5B UNAMGS and how large is it?

Miniclaus Qw1.5B UNAMGS is developed by fblgit, a model with 1.5b 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 Miniclaus Qw1.5B UNAMGS?

Miniclaus Qw1.5B UNAMGS is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Miniclaus Qw1.5B UNAMGS by fblgit — VRAM 3.5GB, 128K context

Name: Miniclaus Qw1.5B UNAMGS
Author: fblgit

Miniclaus Qw1.5B UNAMGS is an open-source language model by fblgit. Features: 1.5b LLM, VRAM: 3.5GB, Context: 128K, License: other, Instruction-Based, LLM Explorer Score: 0.2.

Arxiv:2406.08464 Arxiv:2407.10671 Ara Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-1.5b-i... Conversational Dataset:magpie-align/magpie-pr... Deu Endpoints compatible Eng Fra Generated from trainer Instruct Ita Jpn Kor Por Qwen2 Region:us Rus Safetensors Sharded Spa Tensorflow Tha Vie Zho

Model Card on HF 🤗: https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS

Miniclaus Qw1.5B UNAMGS Parameters and Internals

LLM Name	Miniclaus Qw1.5B UNAMGS
Repository 🤗	https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS
Base Model(s)	Qwen/Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct
Model Size	1.5b
Required VRAM	3.5 GB
Updated	2026-08-03
Maintainer	fblgit
Model Type	qwen2
Instruction-Based	Yes
Model Files	3.5 GB: 1-of-1
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Miniclaus Qw1.5B UNAMGS 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 Miniclaus Qw1.5B UNAMGS

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	18	0
Merge Cord V1	128K / 3.1 GB	155	1
Gte Qwen2 1.5B Instruct	128K / 7.1 GB	670633	238
AceInstruct 1.5B	128K / 3.5 GB	139	22
... Abliterated TIES Qwen2.5 1.5B	128K / 3.5 GB	15	0
Saba1 1.8B	128K / 3.6 GB	6	1
Bellatrix 1.5B XElite	128K / 3.5 GB	19	3
GCIRS Reasoning 1.5B R1	128K / 3.5 GB	9	1
...illed Scout 1.5B Instruct Gen1	128K / 3.5 GB	10	0
Deepthink 1.5B Open PRM	128K / 7.1 GB	12	0

Note: green Score (e.g. "73.2") means that the model is better than fblgit/miniclaus-qw1.5B-UNAMGS.