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SAGE MM Qwen2.5 VL 7B SFT GGUF

> The SAGE-MM-Qwen2.5-VL-7B-SFT from allenai is a 7B-parameter vision-language model fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct, serving as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for...

TypeOtherModèle de baseallenai/SAGE-MM-Qwen2.5-VL-7B-SFTVersionSAGE MM Qwen2.5 VL 7 B SFT.f16BClasse

Source du modèle

Type
Other
Taille du fichier
816 MB–14,2 GB
Modèle de base
allenai/SAGE-MM-Qwen2.5-VL-7B-SFT
Version
vSAGE MM Qwen2.5 VL 7 B SFT.f16
Mis à jour
21 oct. 2026
Source
Hugging Face

Description de la source

SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF

The SAGE-MM-Qwen2.5-VL-7B-SFT from allenai is a 7B-parameter vision-language model fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct, serving as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning through a two-stage process: Stage-1 analyzes initial sampled frames and metadata to classify queries as single-turn (immediate answers) or multi-turn (tool-required), while Stage-2 iteratively generates JSON-formatted tool calls for web-search, speech transcription on timestamps, event grounding, video part extraction, and detailed visual analysis to build context progressively [attached_file:1 equivalent]. Designed to handle arbitrary-length videos beyond fixed horizons—like sports events, narratives, or complex timelines—it requires the SAGE GitHub runtime for tool parsing/execution and observation feedback, enabling robust Q&A via dynamic tool orchestration under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations for efficient deployment. This SFT variant powers the SAGE framework's superior performance on benchmarks like MINERVA, outperforming prior Qwen3-VL-4B baselines in extended video comprehension.

SAGE-MM-Qwen2.5-VL-7B-SFT [GGUF]

File NameQuant TypeFile SizeFile Link
SAGE-MM-Qwen2.5-VL-7B-SFT.IQ4_XS.ggufIQ4_XS4.25 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.ggufQ2_K3.02 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.ggufQ3_K_L4.09 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.ggufQ3_K_M3.81 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_S.ggufQ3_K_S3.49 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_M.ggufQ4_K_M4.68 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_S.ggufQ4_K_S4.46 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_M.ggufQ5_K_M5.44 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_S.ggufQ5_K_S5.32 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q6_K.ggufQ6_K6.25 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.Q8_0.ggufQ8_08.1 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.f16.ggufF1615.2 GBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-Q8_0.ggufmmproj-Q8_0856 MBDownload
SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-f16.ggufmmproj-f161.35 GBDownload

Quants Usage

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

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

image.png

Sources

1 source
Hugging FacePrincipalVersion f05cc74ad4bcee296d8d5041d1cdf9778802bcd5
huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF/tree/f05cc74ad4bcee296d8d5041d1cdf9778802bcd5

Vérifié 7 août

Artefacts du modèle

14 artefacts

SAGE-MM-Qwen2.5-VL-7B-SFT.f16.gguf

gguf · 14,2 GB · SHA-256 bedb64f74094…28fa · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.IQ4_XS.gguf

gguf · 3,96 GB · SHA-256 2518012e49a4…a55f · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-f16.gguf

gguf · 1,26 GB · SHA-256 31a21178e75d…d014 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-Q8_0.gguf

gguf · 816 MB · SHA-256 a3ecdd5eb167…77ec · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.gguf

gguf · 2,81 GB · SHA-256 16d264a6c4a7…ffce · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.gguf

gguf · 3,81 GB · SHA-256 a23f036be7b4…5fae · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.gguf

gguf · 3,55 GB · SHA-256 5dc4a0635134…6b05 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_S.gguf

gguf · 3,25 GB · SHA-256 9cbcc685df57…07de · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_M.gguf

gguf · 4,36 GB · SHA-256 c891bafdaa7a…ce18 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_S.gguf

gguf · 4,15 GB · SHA-256 faa0908e3f25…58a2 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_M.gguf

gguf · 5,07 GB · SHA-256 353a527c1067…4039 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_S.gguf

gguf · 4,95 GB · SHA-256 b7b7fac422da…6afe · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q6_K.gguf

gguf · 5,82 GB · SHA-256 2df759d67fb1…b336 · Hugging Face

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SAGE-MM-Qwen2.5-VL-7B-SFT.Q8_0.gguf

gguf · 7,54 GB · SHA-256 4ee9bbfe64a8…2345 · Hugging Face

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Extraits de sources

2 extraits

--- license: apache-2.0 language: - en base_model: - allenai/SAGE-MM-Qwen2.5-VL-7B-SFT pipeline_tag: video-text-to-text library_name: transformers tags: - text-generation-inference - llama.cpp --- # **SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF** > The SAGE-MM-Qwen2.5-VL-7B-SFT from allenai is a 7B-parameter vision-language model fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct, serving as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning through a two-stage process: Stage-1 analyzes initial sampled frames and metadata to classify queries as single-turn (immediate answers) or multi-turn (tool-required), while Stage-2 iteratively generates JSON-formatted tool calls for web-search, speech transcription on timestamps, event grounding, video part extraction, and detailed visual analysis to build context progressively [attached_file:1 equivalent]. Designed to handle arbitrary-length videos beyond fixed horizons—like sports events, narratives, or complex timelines—it requires the SAGE GitHub runtime for tool parsing/execution and observation feedback, enabling robust Q&A via dynamic tool orchestration under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations for efficient deployment. This SFT variant powers the SAGE framework's superior performance on benchmarks like MINERVA, outperforming prior Qwen3-VL-4B baselines in extended video comprehension. ## SAGE-MM-Qwen2.5-VL-7B-SFT [GGUF] | File Name | Quant Type | File Size | File Link | | - | - | - | - | | SAGE-MM-Qwen2.5-VL-7B-SFT.IQ4_XS.gguf | IQ4_XS | 4.25 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT.IQ4_XS.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.gguf | Q2_K | 3.02 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.gguf | Q3_K_L | 4.09 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.gguf | Q3_K_M | 3.81 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_S.gguf | Q3_K_S | 3.49 GB | [Download](https://huggingface.co/prithivMLmo...

Source context: 138 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF