What are the hardware requirements for Polyalign Qwen2.5 3B En Sft?

Polyalign Qwen2.5 3B En Sft requires approximately 6.2 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 Polyalign Qwen2.5 3B En Sft and how large is it?

Polyalign Qwen2.5 3B En Sft is developed by sathiiiii, a model with 3b 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 Polyalign Qwen2.5 3B En Sft?

Polyalign Qwen2.5 3B En Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Polyalign Qwen2.5 3B En Sft by sathiiiii — VRAM 6.2GB, 32K context

Name: Polyalign Qwen2.5 3B En Sft
Author: sathiiiii

Polyalign Qwen2.5 3B En Sft is an open-source language model by sathiiiii. Features: 3b LLM, VRAM: 6.2GB, Context: 32K, License: other, LLM Explorer Score: 0.23.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-3b Conversational Endpoints compatible Full Generated from trainer Llama-factory Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/sathiiiii/polyalign-qwen2.5-3b-en-sft

Polyalign Qwen2.5 3B En Sft Parameters and Internals

LLM Name	Polyalign Qwen2.5 3B En Sft
Repository 🤗	https://huggingface.co/sathiiiii/polyalign-qwen2.5-3b-en-sft
Base Model(s)	Qwen/Qwen2.5-3B Qwen/Qwen2.5-3B
Model Size	3b
Required VRAM	6.2 GB
Updated	2026-08-08
Maintainer	sathiiiii
Model Type	qwen2
Model Files	5.0 GB: 1-of-2 1.2 GB: 2-of-2 0.0 GB
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.56.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Polyalign Qwen2.5 3B En Sft 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 sathiiiii/polyalign-qwen2.5-3b-en-sft.