QwenStoryteller-i1-GGUF Open-source Model - Supports Visual Narrative and Ensures Consistent Storytelling across Frames

Qwenstoryteller I1 GGUF

Developed by mradermacher

QwenStoryteller is a storytelling model based on a vision-language model, focusing on visual narrative and cross-frame consistency.

Image-to-Text EnglishOpen Source License:Apache-2.0 #Visual Storytelling Generation #Cross-frame Consistency #Chain-of-Thought Reasoning

Downloads 340

Release Time : 5/13/2025

Model Overview

This model is primarily used for image-based text generation and storytelling, supporting chain-of-thought reasoning while maintaining cross-frame consistency.

Model Features

Visual Storytelling

Capable of generating coherent narrative stories based on image content.

Cross-frame Consistency

Maintains narrative consistency when processing multi-frame image inputs.

Chain-of-Thought Reasoning

Supports reasoning-based generation through chain-of-thought processes.

Quantization Support

Provides multiple quantized versions to accommodate different hardware requirements.

Model Capabilities

Image-to-text conversion

Story generation

Visual storytelling

Cross-frame consistency maintenance

Use Cases

Content Creation

Image Story Generation

Generates coherent stories based on input image sequences.

Education

Visual-assisted Learning

Helps learners understand and generate related stories through images.

🚀 QwenStoryteller Quantized Model

This project provides quantized versions of the QwenStoryteller model, enabling more efficient deployment and usage.

📚 Documentation

Model Information

Property	Details
Base Model	daniel3303/QwenStoryteller
Datasets	daniel3303/StoryReasoning
Language	en
Library Name	transformers
License	apache-2.0
Quantized By	mradermacher
Tags	vision-language-model, visual-storytelling, chain-of-thought, grounded-text-generation, cross-frame-consistency, storytelling, image-to-text

About

The weighted/imatrix quants of daniel3303/QwenStoryteller are provided. Static quants are available at mradermacher/QwenStoryteller-GGUF.

Usage

⚠️ Important Note

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: Artefact2's gist

FAQ / Model Request

See 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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