What are the hardware requirements for Model Sft Dare?

Model Sft Dare requires approximately 3.1 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 Model Sft Dare and how large is it?

Model Sft Dare is developed by allout2726, 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 Model Sft Dare?

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

Model Sft Dare by allout2726 — VRAM 3.1GB, 32K context

Name: Model Sft Dare
Author: allout2726

Model Sft Dare is an open-source language model by allout2726. Features: 1.5b LLM, VRAM: 3.1GB, Context: 32K, Instruction-Based, Merged, LLM Explorer Score: 0.23.

Merged Model Arxiv:2311.03099 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-1.5b-i... Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/allout2726/model_sft_dare

Model Sft Dare Parameters and Internals

LLM Name	Model Sft Dare
Repository 🤗	https://huggingface.co/allout2726/model_sft_dare
Base Model(s)	Qwen/Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct
Merged Model	Yes
Model Size	1.5b
Required VRAM	3.1 GB
Updated	2026-08-07
Maintainer	allout2726
Model Type	qwen2
Instruction-Based	Yes
Model Files	3.1 GB
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	5.0.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Model Sft Dare 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 Model Sft Dare

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	10	0
Merge Cord V1	128K / 3.1 GB	155	1
Gte Qwen2 1.5B Instruct	128K / 7.1 GB	876389	237
AceInstruct 1.5B	128K / 3.5 GB	139	22
... Abliterated TIES Qwen2.5 1.5B	128K / 3.5 GB	15	0
Saba1 1.8B	128K / 3.6 GB	6	1
Miniclaus Qw1.5B UNAMGS	128K / 3.5 GB	12	8
Bellatrix 1.5B XElite	128K / 3.5 GB	19	3
GCIRS Reasoning 1.5B R1	128K / 3.5 GB	9	1
...illed Scout 1.5B Instruct Gen1	128K / 3.5 GB	10	0

Note: green Score (e.g. "73.2") means that the model is better than allout2726/model_sft_dare.