What are the hardware requirements for Qwen Tulu 3B SFT?

Qwen Tulu 3B 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 Qwen Tulu 3B SFT and how large is it?

Qwen Tulu 3B SFT is developed by phunguyen01, 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 Qwen Tulu 3B SFT?

Qwen Tulu 3B SFT is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen Tulu 3B SFT by phunguyen01 — VRAM 6.2GB, 32K context

Name: Qwen Tulu 3B SFT
Author: phunguyen01

Qwen Tulu 3B SFT is an open-source language model by phunguyen01. Features: 3b LLM, VRAM: 6.2GB, Context: 32K, License: other, LLM Explorer Score: 0.16.

Axolotl Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-3b Conversational Dataset:allenai/tulu-3-sft-mix... Endpoints compatible Generated from trainer Pytorch Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/phunguyen01/Qwen-Tulu-3B-SFT

Qwen Tulu 3B SFT Parameters and Internals

LLM Name	Qwen Tulu 3B SFT
Repository 🤗	https://huggingface.co/phunguyen01/Qwen-Tulu-3B-SFT
Base Model(s)	Qwen/Qwen2.5-3B Qwen/Qwen2.5-3B
Model Size	3b
Required VRAM	6.2 GB
Updated	2026-08-05
Maintainer	phunguyen01
Model Type	qwen2
Model Files	5.0 GB: 1-of-2 1.2 GB: 2-of-2 0.0 GB 0.0 GB
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.47.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen Tulu 3B 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 phunguyen01/Qwen-Tulu-3B-SFT.