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Qwen3.8-Flash-Next-GSQ-RCO-GGUF

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Duplicate from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF

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Co-authored-by: Michael Helcig <helcig@users.noreply.huggingface.co>

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+ ---
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+ base_model: Qwen/Qwen3.8-Flash-Next
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+ base_model_relation: quantized
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+ pipeline_tag: image-text-to-text
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+ library_name: gguf
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+ license: apache-2.0
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+ tags:
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+ - gguf
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+ - gsq
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+ - rco
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+ - quantization
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+ - mixed-precision
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+ - ist-daslab
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+ - moe
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+ - multimodal
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+ - vision
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+ ---
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+
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+ <!--
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+ GSQ-RCO GGUF release card for Qwen3.8-Flash-Next.
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+ Mirrors the Qwen3.8-27B card.
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+ NB: the YAML block must remain the very first bytes of the file (HF requirement).
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+ -->
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+
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+ <div align="center">
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+
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+ <a href="https://github.com/IST-DASLab"><img src="assets/banner.png" alt="GGUF, GSQ-RCO dynamic non-uniform quantization" width="100%"/></a>
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+
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+ <br/>
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+
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+ # Qwen3.8-Flash-Next &middot; GSQ-RCO GGUFs
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+
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+ **Non-uniform GGUF quantizations** of a 512-expert MoE, produced with **GSQ** and **RCO**, with a vision projector for multimodal use.
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+
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+ [![arXiv: GSQ](https://img.shields.io/badge/arXiv-GSQ_2604.18556-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2604.18556)
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+ [![arXiv: RCO](https://img.shields.io/badge/arXiv-RCO_2605.00649-b31b1b?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2605.00649)
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+ [![GSQ code](https://img.shields.io/badge/code-GSQ-181717?logo=github&logoColor=white)](https://github.com/IST-DASLab/GSQ)
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+ [![RCO code](https://img.shields.io/badge/code-RCO-181717?logo=github&logoColor=white)](https://github.com/IST-DASLab/RCO)
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+ [![DASLab](https://img.shields.io/badge/DASLab-GitHub-101048?logo=github&logoColor=white)](https://github.com/IST-DASLab)
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+ [![license](https://img.shields.io/badge/license-apache--2.0-19a34a)](#license)
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+
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+ </div>
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+
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+ ![Task average vs bit-width](assets/plots/Qwen3.8-Flash-Next-task_avg_vs_avg_bit_width.png)
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+
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+ ---
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+
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+ ## Overview
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+
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+ This repository provides GGUF quantizations of **Qwen3.8-Flash-Next** at four sizes, together with the model's vision projector (`mmproj`) for multimodal use. In contrast to uniform quantization, which applies a single quantization type to all weight tensors, each model here assigns a separate quantization type to every tensor. The assignment is obtained by a gradient-based search that allocates precision according to per-tensor sensitivity, subject to a total size budget. The resulting files are standard GGUF and run unmodified in `llama.cpp`, Ollama, and LM Studio.
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+
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+ A code-specialized variant is released separately as [Qwen3.8-Flash-Next-GSQ-RCO-Coder](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF), which retains 256 of the 512 routed experts per layer at an effective 1.89 bpw over the original parameters, for a 58.4 GB total.
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+
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+ Flash-Next is a sparse mixture-of-experts model: **512 routed experts per layer across 48 layers**, of which 10 are active per token. The routed expert matrices dominate the parameter count, so they also dominate the size budget: 95% of the searchable weights live in the experts. The search therefore spends most of its freedom there, assigning a quantization type **per layer** (not per expert, which GGUF cannot express).
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+
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+ > **Method summary.** GSQ provides accurate low-bit scalar quantization of each tensor at a given quantization type; RCO assigns the per-tensor quantization types under a size budget. Together they yield a non-uniform GGUF at the requested size.
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+
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+ | Method | Description |
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+ |---|---|
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+ | **GSQ** (Gumbel-Softmax Quantization, [paper](https://arxiv.org/abs/2604.18556), [code](https://github.com/IST-DASLab/GSQ)) | Post-training scalar quantization that jointly learns the per-coordinate grid assignments and the per-group scales via a Gumbel-Softmax relaxation. GSQ closes most of the gap between scalar and vector quantization at 2 to 3 bits while remaining deployable in standard scalar formats such as GGUF. |
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+ | **RCO** (Riemannian Constrained Optimization, [paper](https://arxiv.org/abs/2605.00649), [code](https://github.com/IST-DASLab/RCO)) | Assigns one of K quantization types to each of N tensors under a total size budget. The budget constraint is reformulated as a smooth Riemannian manifold in logit space, which permits gradient-based optimization directly on the task loss while enforcing the budget exactly, without constraint-specific hyperparameter tuning. |
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+
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+ Both methods were developed at the [Deep Algorithms and Systems Lab (DASLab)](https://github.com/IST-DASLab), Institute of Science and Technology Austria.
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+
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+ ---
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+
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+ ## Available files
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+
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+ Files follow the convention **`<model>-GSQ-RCO-<type>.gguf`**, where the suffix names the quantization class; the table lists each file's average bit-width over the transformer weights.
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+
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+ The `mmproj` file carries the vision encoder and projector at BF16; one copy serves all quantizations. Each quantized model ships as **two shards**. The second shard is identical across all four variants: it holds the per-layer n-gram embedding table (`per_layer_token_embd`, 51.2B parameters) at IQ4_NL. That table is a lookup, not a matmul weight, so it is held at a fixed 4.5 bpw in every build and excluded from the search. It is quantized in the evaluated models, not just the shipped ones.
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+
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+ | Directory | bpw | Shard 1 (weights) | Shard 2 (n-gram) | Total | Notes |
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+ |---|---|---|---|---|---|
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+ | `Q2_0/` | 2.40 | 37.6 GB | 28.8 GB | **66.4 GB** | Fastest; avoids lookup-table formats |
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+ | `IQ2_XS/` | 2.50 | 39.2 GB | 28.8 GB | **68.0 GB** | Smallest at equal quality |
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+ | `IQ3_XXS/` | 3.00 | 47.0 GB | 28.8 GB | **75.8 GB** | Matches the base model on AIME25 |
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+ | `IQ3_S/` | 3.50 | 54.8 GB | 28.8 GB | **83.6 GB** | Recommended; matches or exceeds the base model on every task |
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+ | `mmproj-Qwen3.8-Flash-Next-BF16.gguf` | 16 | 0.91 GB | n/a | **0.91 GB** | Vision encoder + projector, for multimodal use |
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+
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+ **Q2_0** is built for speed. It avoids the quantization formats that rely on large lookup tables: those formats pack more accuracy into a given bit-width, but decoding them costs real time, and on this model that cost dominates inference. Q2_0 delivers **3.4x the prompt throughput** and **1.9x lower end-to-end latency** than IQ2_XS at a slightly smaller file size, and its decode rate stays flat across workloads instead of varying with the content. The trade is a little quality: 89.07 task average against 89.16 for IQ2_XS, and 3.5 points below IQ3_XXS. Pick it when throughput matters most, and see [Performance](#performance) for the measurements.
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+
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+ The IQ3_S model is the strongest operating point: it matches the base model exactly on AIME25 (100.00), exceeds it on GPQA-Diamond (92.93 against 91.92) and is within 0.57 points on LiveCodeBench v6, at under a quarter of the BF16 size. IQ3_XXS remains the better choice when memory is tight: it gives up 1.52 points on GPQA-Diamond and 0.57 on LiveCodeBench v6 for 7.8 GB less.
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+
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+ ### Memory requirements
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+
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+ Only the first shard holds transformer weights and needs to be resident. The second shard is the n-gram lookup table: it is read sparsely, one row per token, so it can stay memory-mapped on disk and does not need to occupy VRAM. Run with `-lm mmap --lazy-mode on` to get this behaviour, plan for **shard 1 in VRAM**, and keep shard 2 on an SSD so the paging stays cheap.
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+
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+ | Model | Must be resident (shard 1) | Can stay on disk (shard 2) | Total download |
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+ |---|---|---|---|
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+ | `Q2_0` | 37.6 GB | 28.8 GB | 66.4 GB |
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+ | `IQ2_XS` | 39.2 GB | 28.8 GB | 68.0 GB |
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+ | `IQ3_XXS` | 47.0 GB | 28.8 GB | 75.8 GB |
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+ | `IQ3_S` | 54.8 GB | 28.8 GB | 83.6 GB |
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+
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+ Add headroom for the KV cache and the vision projector (0.91 GB) if used. Keeping the n-gram table in RAM rather than on disk removes the paging cost entirely and is worth it where memory allows.
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+
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+ ---
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+
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+ ## Performance
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+
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+ Measured with `llama.cpp` over 55 prompts spanning eleven categories (coding, humanities, math, QA, RAG, reasoning, STEM, writing, multilingual, summarization, roleplay).
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+
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+ | Model | Prompt t/s | Decode t/s | Avg latency |
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+ |---|---|---|---|
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+ | `Q2_0` | **367.49** | **93.79** | **6.70 s** |
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+ | `IQ2_XS` | 108.19 | 70.30 | 12.68 s |
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+
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+ ![Prefill throughput by category](assets/plots/Qwen3.8-Flash-Next-prefill_throughput.png)
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+
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+ The gap comes from the quantization formats rather than the size: the two files differ by only 1.6 GB, and Q2_0 is the smaller of the two. Q2_0's decode rate is also far more stable across categories, 92.5 to 94.5 t/s (a 2.0 t/s spread), where IQ2_XS swings from 49.4 to 94.5 t/s (a 45.1 t/s spread), because the lookup cost varies with how much of each layer a request touches.
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+
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+ The prefill advantage is uneven, and largest where prompts are long: 9.6x on RAG, 6.8x on writing and 6.2x on coding. On short-prompt, reasoning-heavy categories the two are level or Q2_0 is marginally behind (0.8x to 0.9x on stem, math and reasoning), where prefill is too brief for the format's decoding cost to matter.
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+
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+ ---
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+
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+ ## Results
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+
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+ All models are evaluated against the **BF16** base model. We report the average over five zero-shot tasks (arc_easy, arc_challenge, hellaswag, winogrande, piqa), recovery (zero-shot average relative to BF16), and three reasoning and generation benchmarks: **AIME25**, **GPQA-Diamond**, and **LiveCodeBench v6**. Task average is the mean of the three. Sizes are the full download, including the n-gram shard; the bit-width column is the average over the transformer weights, which is what the search controls.
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+
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+ | Variant | bpw | GB | ZS avg↑ | recovery | AIME25↑ | GPQA-D↑ | LCB v6↑ | Task avg↑ |
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+ |---|---|---|---|---|---|---|---|---|
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+ | BF16 | 16.00 | 354 | 76.94 | 100.0% | 100.00 | 91.92 | 87.43 | 93.12 |
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+ | **GSQ-RCO Q2_0** | 2.40 | 66.4 | **78.00** | **101.4%** | 96.67 | 89.39 | 81.14 | 89.07 |
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+ | **GSQ-RCO IQ2_XS** | 2.50 | 68.0 | 77.16 | 100.3% | 96.67 | 87.37 | 83.43 | 89.16 |
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+ | **GSQ-RCO IQ3_XXS** | 3.00 | 75.8 | 77.23 | 100.4% | **100.00** | 91.41 | 86.29 | 92.57 |
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+ | **GSQ-RCO IQ3_S** | 3.50 | 83.6 | n/a | n/a | **100.00** | **92.93** | **86.86** | **93.26** |
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+
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+ All three models exceed the BF16 base on the zero-shot average (100.3% to 101.4%), which is common for careful low-bit quantization on these five tasks: the differences are within roughly one standard error and should be read as parity rather than improvement. The same applies where IQ3_S exceeds the base on GPQA-Diamond and on the task average: recoveries slightly above 100% reflect benchmark variance, not a model that is better than the one it was quantized from. The reasoning and generation benchmarks separate the models far more clearly.
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+
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+ At 3.00 bpw, IQ3_XXS reaches **99.4%** of the base model's task average (92.57 against 93.12) while being **4.7x smaller** (75.8 GB against 354 GB). It matches the base exactly on AIME25 (100.00), and trails by 0.51 on GPQA-Diamond and 1.14 on LiveCodeBench v6. At 2.50 bpw, IQ2_XS retains 95.7% of the task average at 68.0 GB, and the smallest build, Q2_0, holds 96.67 on AIME25 at 66.4 GB, a 5.3x reduction.
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+
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+ The two sub-2.5 bpw builds land within 0.1 points of each other on task average despite different allocations: Q2_0 is stronger on GPQA-Diamond (89.39 against 87.37) while IQ2_XS is stronger on LiveCodeBench v6 (83.43 against 81.14).
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+
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+ ![AIME25 vs bit-width](assets/plots/Qwen3.8-Flash-Next-aime25_vs_avg_bit_width.png)
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+
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+ ![GPQA-Diamond vs bit-width](assets/plots/Qwen3.8-Flash-Next-gpqa_diamond_vs_avg_bit_width.png)
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+
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+ ![LiveCodeBench v6 vs bit-width](assets/plots/Qwen3.8-Flash-Next-lcb_vs_avg_bit_width.png)
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+
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+ ---
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+
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+ ## Usage
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+
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+ Both shards must be downloaded; `llama.cpp` loads the split automatically when given the first one.
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+
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+ ### llama.cpp
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+ ```bash
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+ # download (requires: pip install -U "huggingface_hub[cli]")
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+ hf download <this-repo> --include "IQ3_XXS/*" --local-dir .
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+
152
+ llama-cli -m IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf \
153
+ -lm mmap --lazy-mode on \
154
+ -p "Explain mixed-precision quantization." -ngl 99
155
+ ```
156
+ `-lm mmap --lazy-mode on` keeps the n-gram table memory-mapped on disk instead of loading it into memory, which removes 28.8 GB from the resident footprint. See [Memory requirements](#memory-requirements).
157
+
158
+ ### Strata Engine
159
+
160
+ Many users report strong results with [Strata](https://github.com/Niko1221/Strata), a third-party engine that runs these GGUFs on a single NVIDIA GPU from 8 GB of VRAM, with a one-click installer and an OpenAI-compatible endpoint on localhost.
161
+
162
+ ### Vision (multimodal)
163
+ ```bash
164
+ hf download <this-repo> mmproj-Qwen3.8-Flash-Next-BF16.gguf --local-dir .
165
+
166
+ llama-mtmd-cli -m IQ3_XXS/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf \
167
+ --mmproj mmproj-Qwen3.8-Flash-Next-BF16.gguf \
168
+ -lm mmap --lazy-mode on \
169
+ --image photo.jpg -p "Describe this image."
170
+ ```
171
+ The projector was converted directly from the base checkpoint and is shared by all four quantizations.
172
+
173
+ ### Ollama
174
+ ```bash
175
+ ollama run hf.co/<this-repo> # pick the directory matching your memory budget
176
+ ```
177
+
178
+ ### LM Studio
179
+ Search the repo name, then pick a `GSQ-RCO-*` build from the file list.
180
+
181
+ ---
182
+
183
+ ## Quantization procedure
184
+
185
+ 1. **Per-tensor database.** Each weight tensor is quantized at every candidate GGUF quantization type with GSQ, yielding a searchable database of quantized tensor variants.
186
+ 2. **RCO search.** The budget-constrained Riemannian search assigns one quantization type per tensor such that the whole-file average bit-width meets the target.
187
+ 3. **Assembly.** The selected per-tensor variants are stitched into a single standard GGUF file.
188
+
189
+ For this model the search covers **352 tensors**: the 304 dense tensors (attention, SSM projections, shared experts, embeddings, output head) and the 48 fused routed-expert matrices, one per layer. Two classes draw on separate quantization ladders, because the routed experts sit at a much lower bit-width than the dense path at any given budget. `ffn_down_exps` searches over a much smaller ladder than the other expert matrices. Its 640 rows are not divisible by 256, which rules out every block-256 K and I quant format and leaves only Q2_0 (block 64) and IQ4_NL (block 32). In the two smaller builds every layer lands on Q2_0; in IQ3_XXS the search lifts 18 of the 48 layers to IQ4_NL.
190
+
191
+ Reference implementations: **GSQ** at [IST-DASLab/GSQ](https://github.com/IST-DASLab/GSQ) and **RCO** at [IST-DASLab/RCO](https://github.com/IST-DASLab/RCO).
192
+
193
+ ### Reproducibility artifacts
194
+
195
+ Each released GGUF ships the files needed to audit how it was built:
196
+
197
+ | File | Contents |
198
+ |---|---|
199
+ | `tensor-allocation/<model>.rco-allocation.txt` | The quantization type assigned to every tensor in that file, with a quant-type histogram and the target bit-width. This is the RCO search result, so the allocation can be inspected without opening the model. |
200
+
201
+ ---
202
+
203
+ ## Citation
204
+
205
+ If you use these models or methods, please cite both papers:
206
+
207
+ ```bibtex
208
+ @article{gsq2026,
209
+ title = {GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling},
210
+ author = {Dadgarnia, Alireza and Tabesh, Soroush and Nikdan, Mahdi and Helcig, Michael and Kurtic, Eldar and Kleinegger, Maximilian and Alistarh, Dan},
211
+ journal= {arXiv preprint arXiv:2604.18556},
212
+ year = {2026}
213
+ }
214
+ @article{rco2026,
215
+ title = {Model Compression with Exact Budget Constraints via Riemannian Manifolds},
216
+ author = {Helcig, Michael and Alistarh, Dan},
217
+ journal= {arXiv preprint arXiv:2605.00649},
218
+ year = {2026}
219
+ }
220
+ ```
221
+
222
+ ---
223
+
224
+ ## Acknowledgements
225
+
226
+ We thank [Verda](https://verda.com/) and Scientific Computing at the Institute of Science and Technology Austria for providing the compute resources used to produce these models.
227
+
228
+ ---
229
+
230
+ ## License
231
+
232
+ These quantized weights inherit the license of the base model (**Qwen3.8-Flash-Next**). The GSQ-RCO tooling is released by the Deep Algorithms and Systems Lab under its repository license.
233
+
234
+ <div align="center">
235
+ <sub>Built with <b>GSQ</b> and <b>RCO</b> at the <a href="https://github.com/IST-DASLab">Deep Algorithms and Systems Lab</a> &middot; Institute of Science and Technology Austria</sub>
236
+ </div>
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1
+ # RCO per-tensor GGUF allocation
2
+ # Repository: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
3
+ # GGUF: Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf
4
+ # Source: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/resolve/main/Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf
5
+ # Target whole-file bpw: 2.50
6
+ # GGUF version: 3
7
+ # GGUF metadata entries: 73
8
+ # Tensor count: 1223
9
+ # Quant-type counts: BF16=484, F16=1, F32=292, IQ1_M=6, IQ2_S=68, IQ2_XXS=22, IQ3_S=44, IQ4_NL=36, IQ4_XS=181, Q2_0=53, Q6_K=29, Q8_0=7
10
+ # Format: <GGUF tensor name>: <GGML quantization/storage type>
11
+ # Includes fixed non-RCO tensors (e.g. F32/BF16) so the released GGUF is fully reproducible.
12
+ #
13
+ output.weight: IQ4_XS
14
+ output_hc_down.weight: BF16
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+ blk.0.attn_gate.weight: IQ3_S
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+ blk.0.ffn_down_exps.weight: Q2_0
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956
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965
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967
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974
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977
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978
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979
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+ blk.37.ssm_dt.bias: F32
981
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982
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983
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985
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986
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988
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989
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990
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991
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992
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993
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994
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995
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997
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998
+ blk.38.hc_ffn_inject.weight: BF16
999
+ blk.38.hc_ffn_norm.weight: F32
1000
+ blk.38.hc_ffn_up.weight: BF16
1001
+ blk.38.ssm_a: F32
1002
+ blk.38.ssm_alpha.weight: BF16
1003
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1004
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1005
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1006
+ blk.38.ssm_norm.weight: F32
1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
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1015
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1016
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1017
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1018
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1019
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1020
+ blk.39.ffn_up_exps.weight: IQ2_S
1021
+ blk.39.ffn_up_shexp.weight: IQ4_XS
1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
+ blk.40.hc_ffn_inject.weight: BF16
1050
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1051
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1052
+ blk.40.ssm_a: F32
1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
+ blk.41.ffn_gate_inp_shexp.weight: BF16
1066
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1067
+ blk.41.ffn_up_exps.weight: IQ2_S
1068
+ blk.41.ffn_up_shexp.weight: Q6_K
1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
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1128
+ blk.43.hc_ffn_inject.weight: BF16
1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1160
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1161
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1162
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1163
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1164
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1165
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1166
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1167
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1168
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1169
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1170
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1171
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1172
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1173
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1174
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1175
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1176
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1177
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1178
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1179
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1180
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1181
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1182
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1183
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1184
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
+ blk.46.ssm_beta.weight: BF16
1206
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1207
+ blk.46.ssm_dt.bias: F32
1208
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1209
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1210
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1211
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1212
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1213
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1214
+ blk.47.attn_q_norm.weight: F32
1215
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1216
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1217
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1218
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1219
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1220
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1221
+ blk.47.ffn_gate_shexp.weight: IQ4_XS
1222
+ blk.47.ffn_up_exps.weight: IQ2_S
1223
+ blk.47.ffn_up_shexp.weight: IQ4_XS
1224
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1225
+ blk.47.hc_attn_inject.weight: BF16
1226
+ blk.47.hc_attn_norm.weight: F32
1227
+ blk.47.hc_attn_up.weight: BF16
1228
+ blk.47.hc_ffn_down.weight: BF16
1229
+ blk.47.hc_ffn_inject.weight: BF16
1230
+ blk.47.hc_ffn_norm.weight: F32
1231
+ blk.47.hc_ffn_up.weight: BF16
1232
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1233
+ blk.47.indexer.k_proj.weight: BF16
1234
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1235
+ blk.47.indexer.q_proj.weight: BF16
tensor-allocation/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-00001-of-00002.rco-allocation.txt ADDED
@@ -0,0 +1,1235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # RCO per-tensor GGUF allocation
2
+ # Repository: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
3
+ # GGUF: Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-00001-of-00002.gguf
4
+ # Source: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/resolve/main/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-00001-of-00002.gguf
5
+ # Target whole-file bpw: 3.50
6
+ # GGUF version: 3
7
+ # GGUF metadata entries: 67
8
+ # Tensor count: 1223
9
+ # Quant-type counts: BF16=484, F16=1, F32=292, IQ2_S=40, IQ3_S=20, IQ3_XXS=34, IQ4_NL=86, IQ4_XS=45, Q2_0=9, Q4_K=47, Q5_K=35, Q6_K=129, Q8_0=1
10
+ # Format: <GGUF tensor name>: <GGML quantization/storage type>
11
+ # Includes fixed non-RCO tensors (e.g. F32/BF16) so the released GGUF is fully reproducible.
12
+ #
13
+ output.weight: Q6_K
14
+ output_hc_down.weight: BF16
15
+ output_hc_norm.weight: F32
16
+ output_hc_up.weight: BF16
17
+ token_embd.weight: IQ4_XS
18
+ blk.0.attn_gate.weight: Q4_K
19
+ blk.0.attn_qkv.weight: Q6_K
20
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21
+ blk.0.ffn_down_shexp.weight: IQ4_NL
22
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23
+ blk.0.ffn_gate_inp.weight: BF16
24
+ blk.0.ffn_gate_inp_shexp.weight: BF16
25
+ blk.0.ffn_gate_shexp.weight: Q4_K
26
+ blk.0.ffn_up_exps.weight: IQ3_XXS
27
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28
+ blk.0.hc_attn_down.weight: BF16
29
+ blk.0.hc_attn_inject.weight: BF16
30
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31
+ blk.0.hc_attn_up.weight: BF16
32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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50
+ blk.1.ffn_gate_shexp.weight: IQ4_XS
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+ blk.1.ffn_up_exps.weight: IQ2_S
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54
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55
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56
+ blk.1.hc_attn_up.weight: BF16
57
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58
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59
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60
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61
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62
+ blk.1.ple_key.weight: BF16
63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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95
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100
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101
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102
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103
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104
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107
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108
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109
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110
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118
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119
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120
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122
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123
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124
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125
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126
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128
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129
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130
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131
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132
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136
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140
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141
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142
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144
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145
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146
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147
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153
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1200
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1203
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1206
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1216
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1217
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1220
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1221
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1225
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1226
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1227
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1228
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1229
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1231
+ blk.47.hc_ffn_up.weight: BF16
1232
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tensor-allocation/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.rco-allocation.txt ADDED
@@ -0,0 +1,1235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # RCO per-tensor GGUF allocation
2
+ # Repository: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
3
+ # GGUF: Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf
4
+ # Source: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/resolve/main/Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf
5
+ # Target whole-file bpw: 3.00
6
+ # GGUF version: 3
7
+ # GGUF metadata entries: 67
8
+ # Tensor count: 1223
9
+ # Quant-type counts: BF16=484, F16=1, F32=292, IQ2_S=20, IQ2_XS=20, IQ2_XXS=18, IQ3_S=82, IQ3_XXS=12, IQ4_NL=58, IQ4_XS=67, Q2_0=38, Q4_K=45, Q5_K=15, Q6_K=71
10
+ # Format: <GGUF tensor name>: <GGML quantization/storage type>
11
+ # Includes fixed non-RCO tensors (e.g. F32/BF16) so the released GGUF is fully reproducible.
12
+ #
13
+ output.weight: Q5_K
14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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50
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51
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52
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53
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54
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55
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56
+ blk.1.hc_attn_up.weight: BF16
57
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58
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59
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60
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61
+ blk.1.ple_conv1d.weight: F16
62
+ blk.1.ple_key.weight: BF16
63
+ blk.1.ple_norm_conv.weight: F32
64
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65
+ blk.1.ple_norm_query.weight: F32
66
+ blk.1.ple_value.weight: BF16
67
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68
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69
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70
+ blk.1.ssm_conv1d.weight: F32
71
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1159
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1161
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1162
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1163
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1167
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1168
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1172
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1173
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1175
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1176
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1177
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1178
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1181
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1184
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1186
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1190
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1191
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1192
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1193
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
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1206
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1207
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1208
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1209
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1210
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1211
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1212
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1213
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1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
+ blk.47.ffn_gate_shexp.weight: IQ4_XS
1222
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1223
+ blk.47.ffn_up_shexp.weight: IQ4_XS
1224
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1225
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1226
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1227
+ blk.47.hc_attn_up.weight: BF16
1228
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1229
+ blk.47.hc_ffn_inject.weight: BF16
1230
+ blk.47.hc_ffn_norm.weight: F32
1231
+ blk.47.hc_ffn_up.weight: BF16
1232
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1233
+ blk.47.indexer.k_proj.weight: BF16
1234
+ blk.47.indexer.q_norm.weight: F32
1235
+ blk.47.indexer.q_proj.weight: BF16
tensor-allocation/Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.rco-allocation.txt ADDED
@@ -0,0 +1,1235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # RCO per-tensor GGUF allocation
2
+ # Repository: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
3
+ # GGUF: Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.gguf
4
+ # Source: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/resolve/main/Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.gguf
5
+ # Target whole-file bpw: 2.40
6
+ # GGUF version: 3
7
+ # GGUF metadata entries: 73
8
+ # Tensor count: 1223
9
+ # Quant-type counts: BF16=483, F16=1, F32=292, IQ4_NL=7, IQ4_XS=56, Q2_0=202, Q3_K=91, Q4_0=16, Q4_K=38, Q5_0=7, Q5_K=16, Q6_K=12, Q8_0=2
10
+ # Format: <GGUF tensor name>: <GGML quantization/storage type>
11
+ # Includes fixed non-RCO tensors (e.g. F32/BF16) so the released GGUF is fully reproducible.
12
+ #
13
+ output.weight: Q5_K
14
+ output_hc_down.weight: BF16
15
+ output_hc_norm.weight: F32
16
+ output_hc_up.weight: BF16
17
+ token_embd.weight: Q3_K
18
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19
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20
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21
+ blk.0.ffn_down_shexp.weight: Q5_0
22
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23
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24
+ blk.0.ffn_gate_inp_shexp.weight: BF16
25
+ blk.0.ffn_gate_shexp.weight: IQ4_XS
26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
+ blk.1.hc_attn_up.weight: BF16
57
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58
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59
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60
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61
+ blk.1.ple_conv1d.weight: F16
62
+ blk.1.ple_key.weight: Q2_0
63
+ blk.1.ple_norm_conv.weight: F32
64
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65
+ blk.1.ple_norm_query.weight: F32
66
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67
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68
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69
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70
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71
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72
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73
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74
+ blk.2.attn_gate.weight: IQ4_XS
75
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76
+ blk.2.ffn_down_exps.weight: Q2_0
77
+ blk.2.ffn_down_shexp.weight: Q4_0
78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
+ blk.3.attn_k.weight: Q3_K
100
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101
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102
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103
+ blk.3.attn_q_norm.weight: F32
104
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105
+ blk.3.ffn_down_exps.weight: Q2_0
106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
+ blk.3.hc_ffn_inject.weight: BF16
119
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120
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121
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122
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123
+ blk.3.indexer.q_norm.weight: F32
124
+ blk.3.indexer.q_proj.weight: BF16
125
+ blk.4.attn_gate.weight: Q3_K
126
+ blk.4.attn_qkv.weight: IQ4_XS
127
+ blk.4.ffn_down_exps.weight: Q2_0
128
+ blk.4.ffn_down_shexp.weight: Q2_0
129
+ blk.4.ffn_gate_exps.weight: Q2_0
130
+ blk.4.ffn_gate_inp.weight: BF16
131
+ blk.4.ffn_gate_inp_shexp.weight: BF16
132
+ blk.4.ffn_gate_shexp.weight: Q2_0
133
+ blk.4.ffn_up_exps.weight: Q2_0
134
+ blk.4.ffn_up_shexp.weight: Q2_0
135
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136
+ blk.4.hc_attn_inject.weight: BF16
137
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138
+ blk.4.hc_attn_up.weight: BF16
139
+ blk.4.hc_ffn_down.weight: BF16
140
+ blk.4.hc_ffn_inject.weight: BF16
141
+ blk.4.hc_ffn_norm.weight: F32
142
+ blk.4.hc_ffn_up.weight: BF16
143
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144
+ blk.4.ssm_alpha.weight: BF16
145
+ blk.4.ssm_beta.weight: BF16
146
+ blk.4.ssm_conv1d.weight: F32
147
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148
+ blk.4.ssm_norm.weight: F32
149
+ blk.4.ssm_out.weight: IQ4_XS
150
+ blk.5.attn_gate.weight: Q3_K
151
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152
+ blk.5.ffn_down_exps.weight: Q2_0
153
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154
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155
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156
+ blk.5.ffn_gate_inp_shexp.weight: BF16
157
+ blk.5.ffn_gate_shexp.weight: Q2_0
158
+ blk.5.ffn_up_exps.weight: Q2_0
159
+ blk.5.ffn_up_shexp.weight: Q6_K
160
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161
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162
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163
+ blk.5.hc_attn_up.weight: BF16
164
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165
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166
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167
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168
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169
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170
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171
+ blk.5.ssm_conv1d.weight: F32
172
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173
+ blk.5.ssm_norm.weight: F32
174
+ blk.5.ssm_out.weight: IQ4_XS
175
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176
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177
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178
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179
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180
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181
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182
+ blk.6.ffn_gate_shexp.weight: Q2_0
183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
+ blk.7.attn_q.weight: Q2_0
204
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