What are the hardware requirements for DRIVE SFT?

DRIVE SFT requires approximately 131.5 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 DRIVE SFT and how large is it?

DRIVE SFT is developed by tencent, a model with 32b 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 DRIVE SFT?

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

DRIVE SFT by tencent — VRAM 131.5GB, 32K context

Name: DRIVE SFT
Author: tencent

DRIVE SFT is an open-source language model by tencent. Features: 32b LLM, VRAM: 131.5GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.21.

Arxiv:2511.06307 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-32b-in... Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/tencent/DRIVE-SFT

DRIVE SFT Parameters and Internals

LLM Name	DRIVE SFT
Repository 🤗	https://huggingface.co/tencent/DRIVE-SFT
Base Model(s)	Qwen/Qwen2.5-32B-Instruct Qwen/Qwen2.5-32B-Instruct
Model Size	32b
Required VRAM	131.5 GB
Updated	2026-08-08
Maintainer	tencent
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.5 GB: 1-of-29 4.7 GB: 2-of-29 4.5 GB: 3-of-29 4.5 GB: 4-of-29 4.7 GB: 5-of-29 4.5 GB: 6-of-29 4.5 GB: 7-of-29 4.7 GB: 8-of-29 4.5 GB: 9-of-29 4.5 GB: 10-of-29 4.7 GB: 11-of-29 4.5 GB: 12-of-29 4.5 GB: 13-of-29 4.7 GB: 14-of-29 4.5 GB: 15-of-29 4.5 GB: 16-of-29 4.7 GB: 17-of-29 4.5 GB: 18-of-29 4.5 GB: 19-of-29 4.7 GB: 20-of-29 4.5 GB: 21-of-29 4.5 GB: 22-of-29 4.7 GB: 23-of-29 4.5 GB: 24-of-29 4.5 GB: 25-of-29 4.7 GB: 26-of-29 4.5 GB: 27-of-29 4.5 GB: 28-of-29 3.7 GB: 29-of-29
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	float32
Errors	replace

Rank the DRIVE 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

Best Alternatives to DRIVE SFT

Best Alternatives	Context / RAM	Downloads	Likes
...y Qwen2.5coder 32B V24.1q 200K	195K / 65.8 GB	9	2
...wen2.5 32B Inst BaseMerge TIES	128K / 65.8 GB	42	17
...wen2.5 32B Inst BaseMerge TIES	128K / 65.8 GB	13	4
Franqwenstein 35B	128K / 69.8 GB	5	8
ELYZA Thinking 1.0 Qwen 32B	128K / 65.8 GB	23	10
Archaeo 32B KTO	128K / 65.8 GB	11	4
Hamanasu Magnum QwQ 32B	128K / 65.8 GB	295	9
Qwen2.5 32B Gokgok Step3	128K / 65.7 GB	6	0
Openthaigpt R1 32B Instruct	128K / 65.8 GB	33	8
Qwen2.5 32B Dark Days Stage2	128K / 65.8 GB	9	0

Note: green Score (e.g. "73.2") means that the model is better than tencent/DRIVE-SFT.