Downloads · 30 days
394
8% of all-time downloads
prithivMLmods/Q3.5-9B-Opus-DA-GGUF
Q3.5-9B-Opus-DA-GGUF is a image-text-to-text model from prithivMLmods. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
Q3.5-9B-Opus-DA (Qwen3.5 Distilled-Abliterated) is a reasoning-focused model built on Qwen/Qwen3.5-9B. This model is optimized for rich, detailed, and context-aware reasoning, leveraging multi-stage training on Opus r…
Downloads · 30 days
394
8% of all-time downloads
All-time downloads
4.8K
Public
Repo size
145 GB
Likes
3
Public
Click a slice to open those files.
.gguf146 GB · 100%
From the Hugging Face model README
Q3.5-9B-Opus-DA (Qwen3.5 Distilled-Abliterated) is a reasoning-focused model built on Qwen/Qwen3.5-9B. This model is optimized for rich, detailed, and context-aware reasoning, leveraging multi-stage training on Opus reasoning traces combined with advanced refusal direction analysis and ablation-based training strategies to reduce internal refusal behaviors while preserving strong reasoning and instruction-following performance.
[!IMPORTANT] This model is intended strictly for research and learning purposes. Due to reduced internal refusal mechanisms, it may generate sensitive or unrestricted content. Users assume full responsibility for how the model is used. The authors and hosting platform disclaim any liability for generated outputs.
[!NOTE] Note: This model is experimental and may generate artifacts.
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Q3.5-9B-Opus-DA.BF16.gguf | BF16 | 17.9 GB | Download |
| Q3.5-9B-Opus-DA.F16.gguf | F16 | 17.9 GB | Download |
| Q3.5-9B-Opus-DA.F32.gguf | F32 | 35.8 GB | Download |
| Q3.5-9B-Opus-DA.Q2_K.gguf | Q2_K | 3.83 GB | Download |
| Q3.5-9B-Opus-DA.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Download |
| Q3.5-9B-Opus-DA.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| Q3.5-9B-Opus-DA.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| Q3.5-9B-Opus-DA.Q4_0.gguf | Q4_0 | 5.31 GB | Download |
| Q3.5-9B-Opus-DA.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| Q3.5-9B-Opus-DA.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| Q3.5-9B-Opus-DA.Q5_0.gguf | Q5_0 | 6.31 GB | Download |
| Q3.5-9B-Opus-DA.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| Q3.5-9B-Opus-DA.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Download |
| Q3.5-9B-Opus-DA.Q6_K.gguf | Q6_K | 7.36 GB | Download |
| Q3.5-9B-Opus-DA.Q8_0.gguf | Q8_0 | 9.53 GB | Download |
| Q3.5-9B-Opus-DA.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | Download |
| Q3.5-9B-Opus-DA.mmproj-f16.gguf | mmproj-f16 | 922 MB | Download |
| Q3.5-9B-Opus-DA.mmproj-f32.gguf | mmproj-f32 | 1.82 GB | Download |
| Q3.5-9B-Opus-DA.mmproj-q8_0.gguf | mmproj-q8_0 | 624 MB | Download |
(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):
