alesha-pro
Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata
Image input
- Architecture
- controlvector
- License
- Commercial OK
- Base model
- ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
- License
- qwen-community-license-1.0
About Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata
Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata is a GGUF model based on ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF on Hugging Face. GGUF files are published by alesha-pro/Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata-GGUF. It has the controlvector architecture (values written in the GGUF).
There are 4 quantizations, with file sizes from 66.4 GB (Q2_0) to 83.6 GB (IQ3_S) (actual sizes on Hugging Face).
Running Q2_0 (66.4 GB) with an 8,192-token context needs an estimated 67.8 GB of memory, and an estimated 68.5 GB at 32,768 tokens. For 8,192 tokens, a GPU with 64 GB or more of VRAM, or a Mac with 64 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom).
The original model is licensed under Apache License 2.0, which allows commercial use (keep the license text and copyright notice when redistributing). Check the original license for the terms of use. An mmproj file for image input is also available.
File size and estimated memory per quantization
- Up to 8 GB
- Up to 16 GB
- Up to 24 GB
- Up to 48 GB
- Up to 128 GB
- More
File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.
All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).
Using image input: This model supports image input. To use images, an mmproj file is loaded as well.
About the "quality guide" (how far a quantization drifts from the original)
For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.
The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).
These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".
| Quantization | Quality guide | KLD (no imatrix) | KLD (imatrix) | Size (Llama-3-8B) |
|---|---|---|---|---|
| Q8_0 | Nearly identical | 0.0014 | - | 7.96 GiB |
| Q6_K | Very small | 0.0055 | - | 6.14 GiB |
| Q5_K_M | Very small | 0.0108 | - | 5.33 GiB |
| Q5_K_S | Small | 0.0166 | - | 5.21 GiB |
| Q5_1 | Small | 0.0180 | - | 5.65 GiB |
| Q5_0 | Small | 0.0222 | - | 5.21 GiB |
| Q4_K_M | Small | 0.0313 | 0.0282 | 4.58 GiB |
| Q4_K_S | Medium | 0.0431 | 0.0320 | 4.37 GiB |
| IQ4_NL | Small | - | 0.0357 | 4.35 GiB |
| IQ4_XS | Small | - | 0.0363 | 4.14 GiB |
| Q4_1 | Medium | 0.0717 | - | 4.78 GiB |
| Q4_0 | Medium | 0.0719 | - | 4.34 GiB |
| Q3_K_L | Medium | 0.0872 | 0.0731 | 4.03 GiB |
| Q3_K_M | Medium | 0.1019 | 0.0844 | 3.74 GiB |
| IQ3_M | Medium | - | 0.1025 | 3.53 GiB |
| IQ3_S | Medium | - | 0.1113 | 3.42 GiB |
| IQ3_XS | Large | - | 0.1387 | 3.28 GiB |
| IQ3_XXS | Large | - | 0.1836 | 3.05 GiB |
| Q3_K_S | Large | 0.2282 | 0.1998 | 3.41 GiB |
| IQ2_M | Large | - | 0.3260 | 2.74 GiB |
| Q2_K | Very large | 0.4451 | 0.3322 | 2.96 GiB |
| Q2_K_S | Very large | - | 0.4034 | 2.96 GiB |
| IQ2_S | Very large | - | 0.4392 | 2.56 GiB |
| IQ2_XS | Very large | - | 0.5463 | 2.43 GiB |
| IQ2_XXS | Very large | - | 0.8120 | 2.24 GiB |
| IQ1_M | Very large | - | 1.3931 | 2.01 GiB |
| IQ1_S | Very large | - | 2.2546 | 1.88 GiB |
Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations
GGUF source
alesha-pro/Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata-GGUF
Published by the original developer Modified version (name includes abliterated etc.) About this label
Last checked: Nov 11, 2026
- Architecture
- controlvector
- Not supported by mainline llama.cpp (as of b12025)
- License
- apache-2.0 Commercial OK
| Quantization | File size Measured | Quality guide | 4,096 | 8,192 | 16,384 | 32,768 | 65,536 | 131,072 | 262,144 |
|---|---|---|---|---|---|---|---|---|---|
| Q2_0 (2 parts) | 66.4 GB | - | 67.7 GB | 67.8 GB | 68.0 GB | 68.5 GB | 69.4 GB | 71.2 GB | 74.8 GB |
| IQ2_XS (2 parts) | 68.0 GB | Very large | 69.3 GB | 69.4 GB | 69.6 GB | 70.1 GB | 71.0 GB | 72.8 GB | 76.4 GB |
| IQ3_XXS (2 parts) | 75.8 GB | Large | 77.1 GB | 77.2 GB | 77.5 GB | 77.9 GB | 78.8 GB | 80.6 GB | 84.3 GB |
| IQ3_S (2 parts) | 83.6 GB | Medium | 84.9 GB | 85.0 GB | 85.2 GB | 85.7 GB | 86.6 GB | 88.4 GB | 92.0 GB |
The mmproj file for image input (BF16) is 0.91 GB Measured . It is not included in the memory above, so add it if you use images.
This GGUF format (controlvector) does not exist in mainline llama.cpp (as of b12025). Ollama, LM Studio and mainline llama.cpp cannot load it; use the dedicated tool described by the source.
License and commercial use
Apache License 2.0 Commercial OK
Allows commercial use, modification and redistribution. When redistributing, keep the license text and copyright notice, and note changes in modified files.
Based on the license on the original model's Hugging Face page. This differs from the GGUF repository's label (qwen-community-1.0). Quantized files are made from the original model, so check the original model's license terms as well. Original license text (checked Oct 18, 2026)
The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses
Frequently asked questions
- How much memory do I need to run Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata (Q2_0)?
- The file size is 66.4 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 67.8 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
- Can Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata be used commercially?
- The original model's license is Apache License 2.0 (Commercial use allowed).Allows commercial use, modification and redistribution. When redistributing, keep the license text and copyright notice, and note changes in modified files. Check the original license text before deciding.
- Does Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?
- 8 GB: no quantization fits; 12 GB: no quantization fits; 16 GB: no quantization fits; 24 GB: no quantization fits (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).