What are the hardware requirements for Thinkless 1.5B RL DeepScaleR?

Thinkless 1.5B RL DeepScaleR requires approximately 7.1 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Thinkless 1.5B RL DeepScaleR and how large is it?

Thinkless 1.5B RL DeepScaleR is developed by Vinnnf, 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 Thinkless 1.5B RL DeepScaleR?

Thinkless 1.5B RL DeepScaleR is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Thinkless 1.5B RL DeepScaleR by Vinnnf — VRAM 7.1GB, 32K context

Name: Thinkless 1.5B RL DeepScaleR
Author: Vinnnf

Thinkless 1.5B RL DeepScaleR is an open-source language model by Vinnnf. Features: 1.5b LLM, VRAM: 7.1GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.18.

Arxiv:2505.13379 Base model:finetune:vinnnf/thi... Base model:vinnnf/thinkless-1.... Conversational Dataset:agentica-org/deepscale... Endpoints compatible Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR

Thinkless 1.5B RL DeepScaleR Parameters and Internals

LLM Name	Thinkless 1.5B RL DeepScaleR
Repository 🤗	https://huggingface.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR
Base Model(s)	Vinnnf/Thinkless-1.5B-Warmup Vinnnf/Thinkless-1.5B-Warmup
Model Size	1.5b
Required VRAM	7.1 GB
Updated	2026-07-26
Maintainer	Vinnnf
Model Type	qwen2
Model Files	5.0 GB: 1-of-2 2.1 GB: 2-of-2
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.47.1
Tokenizer Class	LlamaTokenizer
Padding Token	<\|EOT\|>
Vocabulary Size	151680
Torch Data Type	float32

Rank the Thinkless 1.5B RL DeepScaleR 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 Thinkless 1.5B RL DeepScaleR

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	18	0
ReaderLM V2	500K / 3.1 GB	35356	804
Reader Lm 1.5B	250K / 3.1 GB	385	608
DeepSeek R1 Distill Qwen 1.5B	128K / 3.5 GB	600051	1545
Pulsar Coder V1	128K / 3.1 GB	291	1
MLR Executor Qwen 1.5B	128K / 3.5 GB	503	0
Axiom Python 1.5B	128K / 3.1 GB	312	0
Ko R1 1.5B Preview	128K / 7.1 GB	8	9
...2 1.5B Lora Tensorboard Merged	128K / 3.1 GB	124	1
Qwen2.5 1.5B	128K / 3.1 GB	863295	203

Note: green Score (e.g. "73.2") means that the model is better than Vinnnf/Thinkless-1.5B-RL-DeepScaleR.