What are the hardware requirements for Qwen3.5 2B Self Reward Autonomy S28?

Qwen3.5 2B Self Reward Autonomy S28 requires approximately 4.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.5 2B Self Reward Autonomy S28 and how large is it?

Qwen3.5 2B Self Reward Autonomy S28 is developed by fjzzq2002, a model with 2b 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 Qwen3.5 2B Self Reward Autonomy S28?

Qwen3.5 2B Self Reward Autonomy S28 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.5 2B Self Reward Autonomy S28 by fjzzq2002 — VRAM 4.4GB

Name: Qwen3.5 2B Self Reward Autonomy S28
Author: fjzzq2002

Qwen3.5 2B Self Reward Autonomy S28 is an open-source language model by fjzzq2002. Features: 2b LLM, VRAM: 4.4GB, License: apache-2.0.

Ai-welfare Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.5-2b Conversational Endpoints compatible Grpo Image-text-to-text Qwen3 5 Region:us Research Safetensors Self-reward

Model Card on HF 🤗: https://huggingface.co/fjzzq2002/qwen3.5-2b-self-reward-autonomy-s28

Qwen3.5 2B Self Reward Autonomy S28 Parameters and Internals

LLM Name	Qwen3.5 2B Self Reward Autonomy S28
Repository 🤗	https://huggingface.co/fjzzq2002/qwen3.5-2b-self-reward-autonomy-s28
Base Model(s)	Qwen/Qwen3.5-2B Qwen/Qwen3.5-2B
Model Size	2b
Required VRAM	4.4 GB
Updated	2026-10-27
Maintainer	fjzzq2002
Model Type	qwen3_5
Model Files	4.4 GB
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.17.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.5 2B Self Reward Autonomy S28 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

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Note: green Score (e.g. "73.2") means that the model is better than fjzzq2002/qwen3.5-2b-self-reward-autonomy-s28.