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...
Modellquelle
- Art
- Other
- Dateigröße
- 717 MB–15,3 GB
- Basismodell
- allenai/SAGE-MM-Qwen3-VL-8B-SFT_RL
- Version
- vRevision cd453ba
- Aktualisiert
- 24. Okt. 2026
- Downloads
- 265
- Quelle
- Hugging Face
Quellenbeschreibung
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 |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.gguf | Q2_K | 3.28 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.gguf | Q3_K_L | 4.43 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.gguf | Q3_K_M | 4.12 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_S.gguf | Q3_K_S | 3.77 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_M.gguf | Q4_K_M | 5.03 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_S.gguf | Q4_K_S | 4.8 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_M.gguf | Q5_K_M | 5.85 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_S.gguf | Q5_K_S | 5.72 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q6_K.gguf | Q6_K | 6.73 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.Q8_0.gguf | Q8_0 | 8.71 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.f16.gguf | F16 | 16.4 GB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-Q8_0.gguf | mmproj-Q8_0 | 752 MB | Download |
| SAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-f16.gguf | mmproj-f16 | 1.16 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
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 24. Okt.
Modellartefakte
14 Artefakte
Modellartefakte
14 ArtefakteSAGE-MM-Qwen3-VL-8B-SFT_RL.f16.gguf
gguf · 15,3 GB · SHA-256 a7f537d88d26…f17a · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.IQ4_XS.gguf
gguf · 4,28 GB · SHA-256 6c7bb3c28266…605c · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-f16.gguf
gguf · 1,08 GB · SHA-256 7aa5ca5e1591…8f58 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.mmproj-Q8_0.gguf
gguf · 717 MB · SHA-256 383127a293d6…4491 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q2_K.gguf
gguf · 3,06 GB · SHA-256 cf451fa1213d…8b71 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_L.gguf
gguf · 4,13 GB · SHA-256 138a53e05ddd…a06b · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_M.gguf
gguf · 3,84 GB · SHA-256 db8b38ff0c5f…f3ba · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q3_K_S.gguf
gguf · 3,51 GB · SHA-256 9ca3f62dafc9…1f39 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_M.gguf
gguf · 4,68 GB · SHA-256 036c25203312…ac72 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q4_K_S.gguf
gguf · 4,47 GB · SHA-256 022588f92eaf…fa3c · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_M.gguf
gguf · 5,45 GB · SHA-256 138d78dd7b52…9841 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q5_K_S.gguf
gguf · 5,33 GB · SHA-256 8b681e29096b…f025 · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q6_K.gguf
gguf · 6,26 GB · SHA-256 05ddd6858f2d…ab3f · Hugging Face
HerunterladenSAGE-MM-Qwen3-VL-8B-SFT_RL.Q8_0.gguf
gguf · 8,11 GB · SHA-256 1d4c8646e64a…8b83 · Hugging Face
HerunterladenQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- 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
