STEVE-R1-7B-SFT-i1-GGUF Open Source Model - Suitable for Free Deployment and Application in Resource-Constrained Environments

STEVE R1 7B SFT I1 GGUF

Developed by mradermacher

This is a weighted/matrix quantized version of the Fanbin/STEVE-R1-7B-SFT model, suitable for resource-constrained environments.

Text-to-Image EnglishOpen Source License:Apache-2.0 #Robot Control #Vision-Language Model #Low-Resource Quantization

Downloads 394

Release Time : 3/22/2025

Model Overview

This model is the quantized version of STEVE-R1-7B-SFT, primarily used for robotics, computer vision, and LLM-related tasks.

Model Features

Multiple Quantization Versions

Offers various quantization versions from IQ1 to Q6_K to meet different hardware requirements

Resource Efficient

The smallest quantized version is only 2GB, suitable for resource-constrained environments

Optimized for Robotics Applications

Specifically optimized for robot control and computer vision tasks

Model Capabilities

Robot control

Computer vision processing

Natural language understanding

Multimodal task processing

Use Cases

Robot Control

Robot Command Understanding

Parse natural language commands and convert them into robot actions

Computer Vision

Visual Scene Understanding

Analyze and understand scenes with visual input

base_model: Fanbin/STEVE-R1-7B-SFT language:

en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags:
robotics
agent
computer-vision
llm

About

weighted/imatrix quants of https://huggingface.co/Fanbin/STEVE-R1-7B-SFT

static quants are available at https://huggingface.co/mradermacher/STEVE-R1-7B-SFT-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	i1-IQ1_S	2.0	for the desperate
GGUF	i1-IQ1_M	2.1	mostly desperate
GGUF	i1-IQ2_XXS	2.4
GGUF	i1-IQ2_XS	2.6
GGUF	i1-IQ2_S	2.7
GGUF	i1-IQ2_M	2.9
GGUF	i1-Q2_K_S	2.9	very low quality
GGUF	i1-Q2_K	3.1	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	3.2	lower quality
GGUF	i1-IQ3_XS	3.4
GGUF	i1-Q3_K_S	3.6	IQ3_XS probably better
GGUF	i1-IQ3_S	3.6	beats Q3_K*
GGUF	i1-IQ3_M	3.7
GGUF	i1-Q3_K_M	3.9	IQ3_S probably better
GGUF	i1-Q3_K_L	4.2	IQ3_M probably better
GGUF	i1-IQ4_XS	4.3
GGUF	i1-IQ4_NL	4.5	prefer IQ4_XS
GGUF	i1-Q4_0	4.5	fast, low quality
GGUF	i1-Q4_K_S	4.6	optimal size/speed/quality
GGUF	i1-Q4_K_M	4.8	fast, recommended
GGUF	i1-Q4_1	5.0
GGUF	i1-Q5_K_S	5.4
GGUF	i1-Q5_K_M	5.5
GGUF	i1-Q6_K	6.4	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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