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):
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Model tree for prithivMLmods/SAGE-MM-Qwen2.5-VL-7B-SFT-GGUF
Base model
Qwen/Qwen2.5-VL-7B-Instruct