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SAGE MM Qwen2.5 VL 7B SFT RL GGUF

> The SAGE-MM-Qwen2.5-VL-7B-SFTRL from allenai is a 7B-parameter reinforcement learning (RL) refined vision-language model, further post-trained via RL from the SAGE-MM-Qwen2.5-VL-7B-SFT (itself fine-tuned from...

ArtOtherBasismodellallenai/SAGE-MM-Qwen2.5-VL-7B-SFT_RLVersionRevision 0660b41BBewertung

Modellquelle

Art
Other
Dateigröße
816 MB–14,2 GB
Basismodell
allenai/SAGE-MM-Qwen2.5-VL-7B-SFT_RL
Version
vRevision 0660b41
Aktualisiert
24. Okt. 2026
Downloads
205
Quelle
Hugging Face

Quellenbeschreibung

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

The SAGE-MM-Qwen2.5-VL-7B-SFT_RL from allenai is a 7B-parameter reinforcement learning (RL) refined vision-language model, further post-trained via RL from the SAGE-MM-Qwen2.5-VL-7B-SFT (itself fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct), acting as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning with enhanced performance over SFT baselines through two-stage operation: Stage-1 assesses initial sampled frames/metadata to route queries as single-turn (direct answers) or multi-turn (tool-needed), while Stage-2 loops JSON-formatted tool calls for web-search, timestamped ASR transcription, event grounding, video frame/subclip extraction, and visual analysis to iteratively build context until resolution. Tailored for arbitrary-length video Q&A across sports, narratives, events, or timelines beyond fixed horizons, it demands the SAGE GitHub runtime for tool parsing/execution/observation feedback, delivering state-of-the-art results on benchmarks like MINERVA under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations enabling efficient deployment.

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

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

Quellen

1 Quelle
Hugging FacePrimärVersion 0660b4134b96a217c6582aa5c7af1ed72eff730b
huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF/tree/0660b4134b96a217c6582aa5c7af1ed72eff730b

Verifiziert 24. Okt.

Modellartefakte

14 Artefakte

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

gguf · 14,2 GB · SHA-256 a1a4f149c447…1ef4 · Hugging Face

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

gguf · 3,96 GB · SHA-256 14d43cfa9253…5e67 · Hugging Face

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

gguf · 1,26 GB · SHA-256 ddbd7faa8938…adeb · Hugging Face

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

gguf · 816 MB · SHA-256 62f398180304…e3c4 · Hugging Face

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

gguf · 2,81 GB · SHA-256 2472184d9ce3…de5a · Hugging Face

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

gguf · 3,81 GB · SHA-256 6f0f1da38f82…e2f0 · Hugging Face

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

gguf · 3,55 GB · SHA-256 0a216cd723e9…3990 · Hugging Face

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

gguf · 3,25 GB · SHA-256 a135f8765da0…9828 · Hugging Face

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

gguf · 4,36 GB · SHA-256 f69d2b61e423…5754 · Hugging Face

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

gguf · 4,15 GB · SHA-256 91f33823bbb5…b1e7 · Hugging Face

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

gguf · 5,07 GB · SHA-256 f861da41a745…b9d2 · Hugging Face

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

gguf · 4,95 GB · SHA-256 67c61c460dde…3d59 · Hugging Face

Herunterladen

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

gguf · 5,82 GB · SHA-256 0513aebdb8d7…e6a8 · Hugging Face

Herunterladen

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

gguf · 7,54 GB · SHA-256 f44283b0ec6c…7c77 · Hugging Face

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Quellenauszüge

3 Auszüge

--- license: apache-2.0 pipeline_tag: video-text-to-text library_name: transformers language: - en base_model: - allenai/SAGE-MM-Qwen2.5-VL-7B-SFT_RL tags: - text-generation-inference - llama.cpp --- # **SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF** > The SAGE-MM-Qwen2.5-VL-7B-SFT_RL from allenai is a 7B-parameter reinforcement learning (RL) refined vision-language model, further post-trained via RL from the SAGE-MM-Qwen2.5-VL-7B-SFT (itself fine-tuned from Qwen/Qwen2.5-VL-7B-Instruct), acting as the core decision-maker in the SAGE (Smart Any-Horizon Agent) system for long video reasoning with enhanced performance over SFT baselines through two-stage operation: Stage-1 assesses initial sampled frames/metadata to route queries as single-turn (direct answers) or multi-turn (tool-needed), while Stage-2 loops JSON-formatted tool calls for web-search, timestamped ASR transcription, event grounding, video frame/subclip extraction, and visual analysis to iteratively build context until resolution. Tailored for arbitrary-length video Q&A across sports, narratives, events, or timelines beyond fixed horizons, it demands the SAGE GitHub runtime for tool parsing/execution/observation feedback, delivering state-of-the-art results on benchmarks like MINERVA under Apache 2.0 for research/educational use per Ai2 guidelines, with GGUF quantizations enabling efficient deployment. ## SAGE-MM-Qwen2.5-VL-7B-SFT_RL [GGUF] | File Name | Quant Type | File Size | File Link | | - | - | - | - | | SAGE-MM-Qwen2.5-VL-7B-SFT_RL.IQ4_XS.gguf | IQ4_XS | 4.25 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT_RL.IQ4_XS.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q2_K.gguf | Q2_K | 3.02 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q2_K.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q3_K_L.gguf | Q3_K_L | 4.09 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q3_K_L.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q3_K_M.gguf | Q3_K_M | 3.81 GB | [Download](https://huggingface.co/prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF/blob/main/SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q3_K_M.gguf) | | SAGE-MM-Qwen2.5-VL-7B-SFT_RL.Q3_K_S.gguf | Q3_K_S | 3.49 GB | [Download](https://huggingface.co/prithivMLmods/SAGE...

Source context: 205 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF

Source context: 45 downloads · 1 likes · Pipeline video-text-to-text · Library transformers · Repo prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT_RL-GGUF