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SAGE MM Qwen3 VL 8B SFT RL GGUF

> The SAGE-MM-Qwen3-VL-8B-SFTRL from allenai is an 8B-parameter reinforcement learning (RL) post-trained vision-language model, refined via RL from the SAGE-MM-Qwen3-VL-8B-SFT (fine-tuned from...

KindOtherBase modelallenai/SAGE-MM-Qwen3-VL-8B-SFT_RLVersionRevision cd453baBgrade

Model source

Kind
Other
File size
717 MB–15.3 GB
Base model
allenai/SAGE-MM-Qwen3-VL-8B-SFT_RL
Version
vRevision cd453ba
Updated
Oct 24, 2026
Downloads
265
Source
Hugging Face

Source description

SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF

The SAGE-MM-Qwen3-VL-8B-SFT_RL from allenai is an 8B-parameter reinforcement learning (RL) post-trained vision-language model, refined via RL from the SAGE-MM-Qwen3-VL-8B-SFT (fine-tuned from Qwen/Qwen3-VL-8B-Instruct), serving as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning with superior performance gains over SFT through two-stage operation: Stage-1 analyzes initial sampled frames/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 (external knowledge), transcribe-speech (timestamped ASR), ground-event (temporal localization), extract-video-parts (high-res frames/subclips), and analyze (visual breakdown) to progressively update context until resolution. Engineered for arbitrary-length video Q&A across sports, narratives, events, or timelines beyond fixed horizons, it requires the SAGE GitHub runtime for tool parsing/execution/observation feedback, achieving state-of-the-art on benchmarks like MINERVA under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations (Q4_K_S/M recommended) for efficient deployment.

SAGE-MM-Qwen3-VL-8B-SFT_RL [GGUF]

File NameQuant TypeFile SizeFile Link
SAGE-MM-Qwen3-VL-8B-SFT_RL.IQ4_XS.ggufIQ4_XS4.59 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.ggufQ2_K3.28 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.ggufQ3_K_L4.43 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.ggufQ3_K_M4.12 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_S.ggufQ3_K_S3.77 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_M.ggufQ4_K_M5.03 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_S.ggufQ4_K_S4.8 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_M.ggufQ5_K_M5.85 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_S.ggufQ5_K_S5.72 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q6_K.ggufQ6_K6.73 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.Q8_0.ggufQ8_08.71 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.f16.ggufF1616.4 GBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-Q8_0.ggufmmproj-Q8_0752 MBDownload
SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-f16.ggufmmproj-f161.16 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 FacePrimaryVersion cd453bab796aaa556986b70377fa27df5c1f5219
huggingface.co/prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF/tree/cd453bab796aaa556986b70377fa27df5c1f5219

Verified Oct 24

Model artifacts

14 artifacts

SAGE-MM-Qwen3-VL-8B-SFT_RL.f16.gguf

gguf · 15.3 GB · SHA-256 a7f537d88d26…f17a · Hugging Face

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SAGE-MM-Qwen3-VL-8B-SFT_RL.IQ4_XS.gguf

gguf · 4.28 GB · SHA-256 6c7bb3c28266…605c · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-f16.gguf

gguf · 1.08 GB · SHA-256 7aa5ca5e1591…8f58 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-Q8_0.gguf

gguf · 717 MB · SHA-256 383127a293d6…4491 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.gguf

gguf · 3.06 GB · SHA-256 cf451fa1213d…8b71 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.gguf

gguf · 4.13 GB · SHA-256 138a53e05ddd…a06b · Hugging Face

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SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.gguf

gguf · 3.84 GB · SHA-256 db8b38ff0c5f…f3ba · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_S.gguf

gguf · 3.51 GB · SHA-256 9ca3f62dafc9…1f39 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_M.gguf

gguf · 4.68 GB · SHA-256 036c25203312…ac72 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_S.gguf

gguf · 4.47 GB · SHA-256 022588f92eaf…fa3c · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_M.gguf

gguf · 5.45 GB · SHA-256 138d78dd7b52…9841 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_S.gguf

gguf · 5.33 GB · SHA-256 8b681e29096b…f025 · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q6_K.gguf

gguf · 6.26 GB · SHA-256 05ddd6858f2d…ab3f · Hugging Face

Download

SAGE-MM-Qwen3-VL-8B-SFT_RL.Q8_0.gguf

gguf · 8.11 GB · SHA-256 1d4c8646e64a…8b83 · Hugging Face

Download

Source excerpts

3 excerpts

--- license: apache-2.0 language: - en base_model: - allenai/SAGE-MM-Qwen3-VL-8B-SFT_RL pipeline_tag: video-text-to-text library_name: transformers tags: - text-generation-inference - llama.cpp --- # **SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF** > The SAGE-MM-Qwen3-VL-8B-SFT_RL from allenai is an 8B-parameter reinforcement learning (RL) post-trained vision-language model, refined via RL from the SAGE-MM-Qwen3-VL-8B-SFT (fine-tuned from Qwen/Qwen3-VL-8B-Instruct), serving as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning with superior performance gains over SFT through two-stage operation: Stage-1 analyzes initial sampled frames/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 (external knowledge), transcribe-speech (timestamped ASR), ground-event (temporal localization), extract-video-parts (high-res frames/subclips), and analyze (visual breakdown) to progressively update context until resolution. Engineered for arbitrary-length video Q&A across sports, narratives, events, or timelines beyond fixed horizons, it requires the SAGE GitHub runtime for tool parsing/execution/observation feedback, achieving state-of-the-art on benchmarks like MINERVA under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations (Q4_K_S/M recommended) for efficient deployment. ## SAGE-MM-Qwen3-VL-8B-SFT_RL [GGUF] | File Name | Quant Type | File Size | File Link | | - | - | - | - | | SAGE-MM-Qwen3-VL-8B-SFT_RL.IQ4_XS.gguf | IQ4_XS | 4.59 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen3-VL-8B-SFT_RL.IQ4_XS.gguf) | | SAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.gguf | Q2_K | 3.28 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.gguf) | | SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.gguf | Q3_K_L | 4.43 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.gguf) | | SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.gguf | Q3_K_M | 4.12 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.gguf) | | SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_S.gguf | Q3_K_S | 3.7...

Source context: 265 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF

Source context: 107 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo prithivMLmods/SAGE-MM-Qwen3-VL-8B-SFT_RL-GGUF