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...
Model source
- Kind
- Other
- File size
- 816 MB–14.2 GB
- Base model
- allenai/SAGE-MM-Qwen2.5-VL-7B-SFT
- Version
- vSAGE MM Qwen2.5 VL 7 B SFT.f16
- Updated
- Oct 21, 2026
- Source
- Hugging Face
Source description
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 |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.gguf | Q2_K | 3.02 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.gguf | Q3_K_L | 4.09 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.gguf | Q3_K_M | 3.81 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_S.gguf | Q3_K_S | 3.49 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_M.gguf | Q4_K_M | 4.68 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_S.gguf | Q4_K_S | 4.46 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_M.gguf | Q5_K_M | 5.44 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_S.gguf | Q5_K_S | 5.32 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q6_K.gguf | Q6_K | 6.25 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.Q8_0.gguf | Q8_0 | 8.1 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.f16.gguf | F16 | 15.2 GB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-Q8_0.gguf | mmproj-Q8_0 | 856 MB | Download |
| SAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-f16.gguf | mmproj-f16 | 1.35 GB | Download |
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
Sources
1 sourceVerified Aug 7
Model artifacts
14 artifacts
Model artifacts
14 artifactsSAGE-MM-Qwen2.5-VL-7B-SFT.IQ4_XS.gguf
gguf · 3.96 GB · SHA-256 2518012e49a4…a55f · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-f16.gguf
gguf · 1.26 GB · SHA-256 31a21178e75d…d014 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.mmproj-Q8_0.gguf
gguf · 816 MB · SHA-256 a3ecdd5eb167…77ec · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q2_K.gguf
gguf · 2.81 GB · SHA-256 16d264a6c4a7…ffce · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_L.gguf
gguf · 3.81 GB · SHA-256 a23f036be7b4…5fae · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_M.gguf
gguf · 3.55 GB · SHA-256 5dc4a0635134…6b05 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q3_K_S.gguf
gguf · 3.25 GB · SHA-256 9cbcc685df57…07de · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_M.gguf
gguf · 4.36 GB · SHA-256 c891bafdaa7a…ce18 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q4_K_S.gguf
gguf · 4.15 GB · SHA-256 faa0908e3f25…58a2 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_M.gguf
gguf · 5.07 GB · SHA-256 353a527c1067…4039 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q5_K_S.gguf
gguf · 4.95 GB · SHA-256 b7b7fac422da…6afe · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q6_K.gguf
gguf · 5.82 GB · SHA-256 2df759d67fb1…b336 · Hugging Face
DownloadSAGE-MM-Qwen2.5-VL-7B-SFT.Q8_0.gguf
gguf · 7.54 GB · SHA-256 4ee9bbfe64a8…2345 · Hugging Face
DownloadSource excerpts
2 excerpts
Source excerpts
2 excerpts--- 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
