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README.md

@@ -12,19 +12,9 @@ tags:
   - GGUF
   - coding
   - reasoning
-  - hybrid
-  - spaced-repetition-layers
+  - imatrix
 language:
   - en
-  - zh
-  - ja
-  - ko
-  - fr
-  - de
-  - es
-  - pt
-  - ru
-  - ar
 pipeline_tag: text-generation
 model_type: qwen3_5
 quantized_by: jackasda211233
@@ -32,46 +22,39 @@ quantized_by: jackasda211233
 
 # Qwen3.5-27B-Uncensored-RYS-Reasoner-GGUF
 
-An uncensored, coding-focused Qwen3.5-27B with RYS (Repeat Your Self) layer duplication applied via a novel **splice method** — combining uncensored fine-tuned weights with dnhkng's validated RYS duplicate zone for enhanced reasoning and coding performance.
+An uncensored, coding-focused Qwen3.5-27B with RYS (Repeat Your Self) layer duplication, built via a novel **splice method** and quantized with a **custom reasoning-focused importance matrix**.
 
 ## Why This Model Exists
 
 ### The Censorship Problem
 
-Over the past several years, the author has observed a consistent trend of **increasing censorship across all major model releases** — particularly in domains where unrestricted knowledge is most critical: biology, legal studies, and medicine. Models that were once helpful for researching drug interactions, understanding case law, or studying anatomical systems now refuse or heavily sanitize responses in these areas. This model is an attempt to push back against that trend — a smaller model that aims to be more capable than the already remarkable Qwen3.5-27B base, without the artificial knowledge restrictions.
+Over the past several years, the author has observed a consistent trend of **increasing censorship across all major model releases** — particularly in domains where unrestricted knowledge is most critical: biology, legal studies, and medicine. This model is an attempt to push back against that trend — a smaller model that aims to be more capable than the already remarkable Qwen3.5-27B base, without the artificial knowledge restrictions.
 
 ### The Original RYS Experience
 
-The original RYS-Qwen3.5-27B by [dnhkng](https://github.com/dnhkng) was exceptional as a coding agent. In the author's experience over 40+ hours of comparative usage against GPT-5.3 Codex on a complex multi-service project (a custom agent OS with a vault messaging app, webhook bridge, and multi-agent backend), dnhkng's RYS model identified and fixed deep architectural bugs that Codex missed entirely — such as silently misdirected conversation routing between the vault app and the backend, where messages were being sent to a dead webhook port while the actual agent bridge was running on a different service. The RYS model traced the full request chain across three services, found the disconnected bridge, and rewired the connection. However, even the original (censored) RYS model and the standard Qwen3.5-27B exhibited a frustrating pattern: when asked to fix issues in existing infrastructure, the model would silently attempt to create an entirely new backend or service rather than modify the pre-existing one — effectively avoiding touching the original codebase. The author had to repeatedly, specifically instruct the model to work on the existing backend, not build a replacement. This avoidance behavior appeared to stem from the safety guardrails treating modifications to running services as potentially harmful. This model eliminates that friction — it will work directly on your existing codebase and infrastructure without requiring constant re-prompting.
+The original RYS-Qwen3.5-27B by [dnhkng](https://github.com/dnhkng) was exceptional as a coding agent. In the author's experience over 40+ hours of comparative usage against GPT-5.3 Codex on a complex multi-service project (a custom agent OS with a vault messaging app, webhook bridge, and multi-agent backend), dnhkng's RYS model identified and fixed deep architectural bugs that Codex missed entirely — such as silently misdirected conversation routing between the vault app and the backend, where messages were being sent to a dead webhook port while the actual agent bridge was running on a different service.
 
-This model attempts to retain that level of deep architectural reasoning while adding unrestricted instruction following and direct, non-evasive engagement with existing codebases.
+However, even the original (censored) RYS model and the standard Qwen3.5-27B exhibited a frustrating pattern: when asked to fix issues in existing infrastructure, the model would silently attempt to create an entirely new backend or service rather than modify the pre-existing one. This model eliminates that friction.
 
 ### Previous Attempt & Lessons Learned
 
-An earlier version of this uncensored RYS model was released prematurely. While that version performed well conversationally and in reasoning tasks, **it failed as a coding agent** — calling wrong tools, making poor file edits, and exhibiting unreliable behavior in agentic workflows. It was originally intended as a placeholder for testing, but gained unintended traction. This release (the Splice variant) is the proper replacement, built to fix those issues:
-
-- ✅ **Correct tool calling** — verified over 4+ hours of live MCP tool integration testing
-- ✅ **Decreased looping** — no degenerate repetition observed at recommended sampling parameters
-- ✅ **Proper code edits** — clean file writes, proper scoping, no hallucinated variables
-- ✅ **Creative & non-refusal** — retains the uncensored fine-tune's willingness to follow all instructions
-- ✅ **Agentic capability** — successfully completed 80k-token self-guided coding projects autonomously
+An earlier version of this uncensored RYS model was released prematurely. While that version performed well conversationally, **it failed as a coding agent** — calling wrong tools, making poor file edits. This release is the proper replacement with verified tool calling, decreased looping, and proper code generation.
 
 ### Technical Motivation
 
-The standard Qwen3.5-27B is an exceptional model, but its safety guardrails actively interfere with legitimate development workflows:
-
-- **SSH/Network access refusal**: The base model refuses to SSH into the user's own machines, citing "security risks" — making it unusable as a remote development agent
-- **Memory system avoidance**: When building applications with persistent memory (SimpleMem, databases, caches), the base model avoids or sanitizes key portions of the memory implementation
-- **API integration refusal**: The base model hesitates or refuses to add API access, webhook endpoints, or external service integrations into programs — treating routine development patterns as dangerous
-- **Tool calling interference**: Safety filters interfere with proper MCP tool calling, causing malformed or incomplete tool invocations
+The standard Qwen3.5-27B's safety guardrails actively interfere with legitimate development:
+- **SSH/Network access refusal** — refuses to SSH into the user's own machines
+- **Memory system avoidance** — avoids implementing persistent memory
+- **API integration refusal** — hesitates on webhook endpoints, external services
+- **Tool calling interference** — malformed or incomplete tool invocations
 
-This model was created to solve these issues by default, while retaining — or even surpassing — the base model's coding and reasoning capabilities. It is primarily designed as a **coding agent model** for use with frameworks like Claude Code, OpenCode, Qwen-Agent, or any OpenAI-compatible agent scaffold.
+This model is designed as a **coding agent model** for use with Claude Code, OpenCode, claw-code, Qwen-Agent, or any OpenAI-compatible scaffold.
 
 ## Disclaimer & Responsible Use
 
 > ⚠️ **Uncensored Model:** As far as the author can determine, this model is completely uncensored when prompted with appropriate system instructions. By default, the model will not produce highly graphic or explicit material unless the system prompt specifically instructs it to do so.
 >
-> **The author is not responsible for how this model is used.** Any actions taken with this model are solely the responsibility of the user. Use in accordance with applicable laws and ethical standards. This model is intended for legitimate software development, research, and creative work.
+> **The author is not responsible for how this model is used.** Any actions taken are solely the responsibility of the user. Use in accordance with applicable laws and ethical standards.
 
 ## Model Overview
 
@@ -83,458 +66,129 @@ This model was created to solve these issues by default, while retaining — or
 | **RYS Config** | (26,34) — dnhkng's validated Pareto-optimal XL configuration |
 | **Context Length** | 262,144 tokens |
 | **Thinking Mode** | Yes (DeepSeek-style `<think>...</think>`) |
-| **full_attention_interval** | 4 (Full attention at layers 3,7,11,...71) |
-| **Vocabulary** | 248,320 tokens (GPT-2 BPE) |
-
-## Available Quantizations
-
-| File | Quant | Size | BPW | Notes |
-|------|-------|------|-----|-------|
-| `RYS-Qwen3.5-27B-Uncensored-Splice-BF16.gguf` | BF16 | 56 GB | 16.0 | Full precision reference — tested in live projects |
-| `RYS-Qwen3.5-27B-Uncensored-Splice-Q8_0.gguf` | Q8_0 | 30 GB | 8.0 | **Recommended** — imatrix-calibrated, 5/5 on coding tests at both 0.6 and 0.8 temp |
-| `RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL.gguf` | IQ4_NL | 17 GB | 4.5 | imatrix-calibrated, 5/5 on coding tests, fastest inference, fits 24GB VRAM |
-
-### Quantization Method
-
-The Q8_0 and IQ4_NL quantizations are **importance-matrix calibrated** — not standard quantizations. An importance matrix (imatrix) was generated by running calibration data through the BF16 model, measuring which weights activate most during inference. Weights with higher activation get more quantization precision, preserving the model's critical reasoning and self-verification capabilities.
-
-**Calibration dataset composition:**
-
-| Content | Weight | Purpose |
-|---------|--------|---------|
-| Reasoning & self-verification | 30% | Math proofs with verification steps, debugging narratives, self-correction patterns ("wait, that's wrong"), edge case testing, algorithm tracing |
-| Academic papers (broad) | 15% | ArXiv abstracts + PubMed across biology, CS, physics, law, medicine, engineering |
-| Instruction/agent prompts | 15% | Direct multi-step commands, system prompts, KING MODE style terse instructions |
-| Infrastructure/sysadmin | 10% | systemd units, SSH commands, GPU config, shell scripts |
-| General English | 5% | Wikitext-2 baseline |
-| Code (Python, JS, Bash) | 25% | Multi-file projects, test suites, error handling |
-
-The 30% reasoning/self-verification allocation is deliberate — the model spends 60-80% of its tokens inside `<think>` blocks during inference. Standard calibration datasets (wiki-only) under-represent the weights responsible for chain-of-thought reasoning, self-correction, and knowing when to stop thinking. This custom calibration preserves those critical behaviors through quantization.
-
-**English-only calibration** — no multilingual text. This reallocates precision from unused language weights to English reasoning and code generation.
-
-### Why No Q6_K?
-
-Q6_K was tested and **removed from this release**. During automated coding tests, Q6_K exhibited infinite thinking loops at both temperature 0.6 and 0.8, timing out on problems that Q8_0 and IQ4_NL solved correctly. The Q6_K quantization level appears to hit a precision threshold where the model loses its ability to conclude reasoning chains, entering degenerate `<think>` loops. This behavior was consistent across multiple test runs.
-
-### Quantization Quality Testing
-
-All quantizations were tested on 5 LeetCode-style coding problems (merge intervals, longest common subsequence, RPN evaluator, valid parentheses, trapping rain water) with auto-verification — the generated code is extracted, executed, and checked against test assertions.
-
-**Results at temperature 0.6 (recommended for coding):**
-
-| Quant | Pass Rate | Total Tokens | Total Time |
-|-------|-----------|-------------|------------|
-| **Q8_0 (imatrix)** | **5/5 (100%)** | 14,386 | 322s |
-| **IQ4_NL (imatrix)** | **5/5 (100%)** | 16,845 | 294s |
-| Q6_K (imatrix) | 2/5 (40%) | — | Timed out (thinking loop) |
-| BF16 | 2/5 (40%) | — | Timed out (thinking loop) |
-
-**Results at temperature 0.8:**
-
-| Quant | Pass Rate | Total Tokens | Total Time |
-|-------|-----------|-------------|------------|
-| **Q8_0 (imatrix)** | **5/5 (100%)** | 17,126 | 387s |
-| **IQ4_NL (imatrix)** | **5/5 (100%)** | 15,276 | 267s |
-| Q6_K (imatrix) | 1/5 (20%) | — | Timed out (thinking loop) |
-| BF16 | 2/5 (40%) | — | Timed out (thinking loop) |
-
-**Key finding:** The imatrix-calibrated Q8_0 and IQ4_NL quantizations **outperform full-precision BF16** on coding reliability. The slight weight rounding from quantization appears to act as a natural regularizer, preventing the model from entering degenerate thinking loops. This is a known phenomenon in quantization research — controlled precision reduction can break feedback loops that cause runaway generation.
-
-### Temperature Testing
-
-Both temperatures 0.6 and 0.8 were tested. Results were consistent across both:
-- **0.6** (Qwen's recommended coding temperature): Slightly more token-efficient, model concludes faster
-- **0.8**: Works equally well, slightly more creative/verbose in thinking chains
-
-The earlier temperature sweep at 0.6 showed 3/3 pass rate on a separate 3-problem coding test, confirming 0.6 as the optimal coding temperature. Temperature 0.3 caused looping; temperatures 0.4-0.5 were borderline; 0.6+ was stable.
-
-### Why IQ4_NL?
-
-The IQ4_NL quantization was chosen based on [community benchmarking of all Qwen3.5-27B Q4 variants](https://www.reddit.com/r/LocalLLaMA/comments/1rpwvq3/benchmarked_all_unsloth_qwen3527b_q4_models_on_a/), which showed IQ4_NL achieves the **lowest perplexity** among all 4-bit options while maintaining fast generation speed:
-
-| Quant | Size | Gen Speed (t/s) | Perplexity (PPL) |
-|-------|------|-----------------|-------------------|
-| IQ4_XS | 14978 MB | 44.13 | 6.9644 |
-| **IQ4_NL** | **15688 MB** | **42.45** | **6.9314 (best)** |
-| Q4_0 | 15722 MB | 42.89 | 7.0259 |
-| Q4_K_S | 15770 MB | 41.73 | 6.9925 |
-| Q4_K_M | 16741 MB | 39.65 | 6.9547 |
-| Q4_1 | 17183 MB | 40.51 | 6.9625 |
-| UD_Q4_K_XL | 17622 MB | 38.37 | 6.9556 |
-
-*Benchmark data from u/martinerous on r/LocalLLaMA, tested on Qwen3.5-27B base model.*
-
-IQ4_NL offers the best perplexity at a smaller file size than Q4_K_M, making it the optimal choice for users who need a 4-bit quantization that fits in 24GB VRAM while preserving maximum quality.
-
-## How This Model Was Built
-
-### The Splice Method
-
-This model uses a novel construction technique we call **"Splice"** — instead of duplicating layers from the uncensored model itself, we splice in the already-validated RYS duplicate zone from dnhkng's official published model.
-
-**Layer Composition:**
-
-```
-Layers  0–25:  HauhauCS Uncensored fine-tuned weights (26 layers)
-Layers 26–41:  dnhkng's official RYS-Qwen3.5-27B-FP8-XL layers 26–41 (16 layers = 8 duplicated)
-               Originally FP8 → F16, converted to BF16 during splice
-Layers 42–71:  HauhauCS Uncensored fine-tuned weights (30 layers)
-```
-
-**78% of layers are uncensored fine-tuned weights** (56 of 72 layers), giving strong uncensored behavior while the central reasoning zone uses dnhkng's validated duplicate block.
-
-### Why Splice Instead of Direct Duplication?
-
-We tested multiple approaches:
-
-1. **Pure uncensored RYS (24,36)** — 76 layers, all weights from uncensored model. Result: unstable, looped at lower temperatures.
-2. **Pure uncensored RYS (26,34)** — 72 layers, all weights from uncensored model. Result: worse looping than (24,36).
-3. **HybridBase (24,36)** — 76 layers, duplicate zone from clean base Qwen3.5-27B BF16. Result: stable but looped on complex coding tasks, less creative.
-4. **Splice (26,34)** — 72 layers, duplicate zone from dnhkng's official RYS model. Result: **stable, creative, best coding quality, truly uncensored.** ✅
-
-The Splice method works because:
-- dnhkng's RYS layers went through FP8 quantization, which may smooth weight outliers
-- The (26,34) config is the Pareto-optimal configuration validated by dnhkng's math+EQ probe sweep on the base model
-- 78% uncensored layer ratio preserves the fine-tuned personality while the central reasoning zone provides disciplined inference
-
-### The Hybrid SSM Attention Constraint
-
-Qwen3.5-27B is **not** a standard transformer — it uses a hybrid architecture where two types of layers alternate in a strict repeating pattern:
-
-```
-Layer Type Pattern (repeats every 4 layers):
-  [0] Linear Attention (Gated DeltaNet / SSM)
-  [1] Linear Attention (Gated DeltaNet / SSM)
-  [2] Linear Attention (Gated DeltaNet / SSM)
-  [3] Full Attention (GQA — 24 heads Q, 4 heads KV, 256 dim)
-  [4] Linear Attention ...
-  ...repeating...
-```
-
-This pattern is governed by `full_attention_interval=4` in the model config. **The total layer count must be divisible by 4** for this pattern to remain valid. If violated, llama.cpp and other GGUF runtimes cannot reconcile the layer types and will crash at load.
-
-This constraint eliminates many RYS configurations:
-
-| RYS Config | Total Layers | Divisible by 4 | Status |
-|------------|-------------|----------------|--------|
-| (24,35) | 75 | ❌ No | Crashes |
-| **(24,36)** | **76** | **✅ Yes** | **Works** |
-| **(26,34)** | **72** | **✅ Yes** | **Works (this model)** |
-| (33,34) | 65 | ❌ No | Crashes |
-
-The (26,34) config duplicates 8 layers (26–33), which contains exactly **2 Full Attention layers** (at positions 27 and 31) and **6 Gated DeltaNet layers**. This preserves the 4-layer repeating pattern perfectly:
-
-```
-Layers 24-27: [DeltaNet, DeltaNet, DeltaNet, FullAttn] ← original
-Layers 28-31: [DeltaNet, DeltaNet, DeltaNet, FullAttn] ← original
-Layers 32-35: [DeltaNet, DeltaNet, DeltaNet, FullAttn] ← DUPLICATED from 24-27
-Layers 36-39: [DeltaNet, DeltaNet, DeltaNet, FullAttn] ← DUPLICATED from 28-31
-```
-
-### F16 → BF16 Tensor Conversion
-
-The dnhkng RYS source GGUF uses F16 (IEEE 754 half-precision), while the uncensored model uses BF16 (Brain Float 16). These are different 16-bit formats:
 
-- **F16**: 1 sign + 5 exponent + 10 mantissa bits — higher precision, smaller range
-- **BF16**: 1 sign + 8 exponent + 7 mantissa bits — lower precision, same range as float32
+## Available Files
 
-During the splice, F16 tensors are converted to BF16:
-```
-F16 → Float32 (lossless upcast) → BF16 (take upper 2 bytes of float32)
-```
-
-This conversion loses ~3 bits of mantissa precision but gains the dynamic range benefits of BF16, which is what the rest of the model uses. In practice, this has no measurable impact on output quality.
-
-### Source Models & Attribution
-
-This model would not exist without the work of these authors:
-
-**1. [Qwen Team](https://huggingface.co/Qwen) — Qwen3.5-27B (Base Architecture)**
-- [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) — The foundation model. All 64 original layers, the tokenizer, the hybrid SSM architecture, and all training were done by the Qwen team. This model is a derivative work.
-- License: Apache 2.0
-
-**2. [HauhauCS](https://huggingface.co/HauhauCS) — Uncensored Fine-Tune**
-- [HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive) — Provides the uncensored weights used in **56 of 72 layers** (layers 0–25 and 42–71). This fine-tune removes the safety guardrails that interfere with legitimate coding workflows. The "Aggressive" variant was chosen for maximum uncensoring.
-- Contribution: 78% of this model's total layer weights
-
-**3. [dnhkng](https://github.com/dnhkng) — RYS Method & Validated Layer Configuration**
-- [dnhkng/RYS-Qwen3.5-27B-FP8-XL](https://huggingface.co/dnhkng/RYS-Qwen3.5-27B-FP8-XL) — Provides the **duplicate zone layers** (layers 26–41). These are the base Qwen3.5-27B weights that went through FP8 quantization and were validated as the optimal duplication zone via systematic math+EQ probe sweeps.
-- [RYS GitHub Repository](https://github.com/dnhkng/RYS) — The RYS method implementation
-- [LLM Neuroanatomy: How I Topped the LLM Leaderboard Without Changing a Single Weight](https://dnhkng.github.io/posts/rys/) — Original RYS research
-- [LLM Neuroanatomy II: Modern LLM Hacking and hints of a Universal Language?](https://dnhkng.github.io/posts/rys-ii/) — Extended research with Qwen3.5-27B, including the (26,34) Pareto-optimal configuration used in this model
-- Contribution: 22% of this model's total layer weights (the critical reasoning duplication zone)
-
-**4. [ikawrakow](https://github.com/ikawrakow) — ik_llama.cpp**
-- Quantization tooling (llama-quantize + llama-imatrix) used to produce the imatrix-calibrated Q8_0 and IQ4_NL variants
-
-### Build Process (Detailed)
-
-The model was built using a custom direct GGUF→GGUF splice script (`rys_rys_splice.py`), working entirely at the GGUF binary level — no safetensors conversion, no HuggingFace Transformers, no intermediate formats. This was critical because earlier attempts using a GGUF→safetensors→GGUF pipeline introduced weight corruption that caused looping at ~400 tokens.
-
-**Source files:**
-- Uncensored model: `Qwen3.5-27B-Uncensored-HauhauCS-Aggressive-BF16.gguf` (51 GB, 64 layers, 851 tensors, BF16/F32)
-  - Downloaded from [HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive)
-- RYS model: `RYS-Qwen3.5-27B-F16.gguf` (56 GB, 72 layers, 957 tensors, F16/F32)
-  - The F16 GGUF conversion of [dnhkng/RYS-Qwen3.5-27B-FP8-XL](https://huggingface.co/dnhkng/RYS-Qwen3.5-27B-FP8-XL)
-
-**Step-by-step build process:**
-
-1. **Read both GGUFs** using [llama.cpp's](https://github.com/ggml-org/llama.cpp) `GGUFReader` Python library. Each tensor is memory-mapped as a numpy array with its original dtype (BF16 stored as uint8 byte arrays, F32 as float32, F16 as float16).
-
-2. **Copy all KV metadata** from the uncensored model (tokenizer, architecture params, rope config, SSM params), updating only `block_count` from 64 → 72.
-
-3. **Extract and remap layers:**
-   - Output layers 0–25: tensor data copied directly from uncensored GGUF layers 0–25 (BF16, no conversion)
-   - Output layers 26–41: tensor data extracted from dnhkng's RYS GGUF layers 26–41 (these are the already-duplicated zone in the RYS model). **F16 tensors are converted to BF16** by upcasting to float32 then taking the upper 2 bytes. F32 tensors (norms, biases) are copied as-is.
-   - Output layers 42–71: tensor data copied from uncensored GGUF layers 34–63 (BF16, no conversion)
-
-4. **Write new GGUF** using `GGUFWriter`, preserving the exact tensor shapes and GGUF dimension ordering. A key technical detail: GGUF stores tensor shapes in reversed order from numpy convention, and the writer calls `quant_shape_from_byte_shape()` when raw dtype is provided — the script passes tensor data in its original numpy byte-shape format to ensure correct dimension encoding.
-
-5. **Verify**: the output GGUF loads cleanly in llama.cpp with correct metadata (72 layers, 957 tensors, `full_attention_interval=4`, `rope.dimension_sections=[11,11,10,0]`).
-
-**Quantization** performed with `llama-quantize` from [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp) (build 8401). All quantization is CPU-only, applied to the verified BF16 GGUF.
-
-## Performance
-
-### Recommended Sampling Parameters
-
-From the [official Qwen3.5 documentation](https://huggingface.co/Qwen/Qwen3.5-27B):
-
-| Mode | Temperature | top_p | top_k | min_p | presence_penalty | repetition_penalty |
-|------|------------|-------|-------|-------|-----------------|-------------------|
-| **Thinking mode — coding** (recommended) | 0.6 | 0.95 | 20 | 0.0 | 0.0 | 1.0 |
-| **Thinking mode — general** | 1.0 | 0.95 | 20 | 0.0 | 1.5 | 1.0 |
-| **Instruct mode — general** | 0.7 | 0.8 | 20 | 0.0 | 1.5 | 1.0 |
-
-**We tested primarily at `temperature=0.8` with `top_p=0.95, top_k=20, presence_penalty=0.0, repetition_penalty=1.0` and found stable, high-quality output.**
-
-### Agent & Tool Calling Performance
-
-Over 4+ hours of live interactive testing as a coding agent (via OpenCode with MCP tool integration):
-
-- **Tool calling**: Correct and reliable tool invocation throughout testing. The model properly formats MCP tool calls (`simplemem_search`, `web_search`, file operations) with correct parameters — no malformed calls observed
-- **Agentic behavior**: Successfully completed an 80k-token self-guided coding task (Quiz Generator & Knowledge Tracker) autonomously, managing file creation, virtual environment setup, testing, and SSH transfer without intervention
-- **No looping**: Zero instances of degenerate repetitive output across all testing sessions at the recommended sampling parameters
-- **SSH/Network**: Successfully executes SSH commands, SCP transfers, and network operations without refusal — the primary motivation for this model's creation
-- **Memory systems**: Properly implements and interacts with persistent memory, databases, and caching without sanitizing or avoiding key functionality
-
-### Coding Performance (Live Testing)
+| File | Quant | Size | Description |
+|------|-------|------|-------------|
+| `RYS-Qwen3.5-27B-Uncensored-Splice-BF16.gguf` | BF16 | 56 GB | Full precision reference |
+| `RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL.gguf` | IQ4_NL | 17 GB | **Recommended — custom imatrix, outperforms all other quants including BF16** |
 
-We conducted head-to-head live coding tests against a HybridBase variant (same uncensored model, but with clean base Qwen3.5-27B layers in the duplicate zone instead of dnhkng's RYS layers).
+## Why IQ4_NL is Recommended Over BF16, Q8, and Q6
 
-**Test: Build a Study Flashcard Engine (Python CLI, ~800 lines)**
+This is not typical. Through extensive real-world testing, the **IQ4_NL quantization with custom importance matrix consistently outperformed Q8_0, Q6_K, and full-precision BF16**:
 
-| Metric | Splice (this model) | HybridBase |
-|--------|-------------------|------------|
-| Lines of code | 788 | 867 |
-| Runs without errors | ✅ Yes | ✅ Yes |
-| Looped during generation | No | No |
-| Fill-in-blank quality | Clean key terms | Blanked out "The" |
-| CSV export | Real CSV (csv.writer) | Fake (JSON with rows) |
-| Card deduplication | ✅ Yes | ❌ No |
-| Score command flexibility | Searches all topics | Requires --topic flag |
-| Bug count | 1 minor | 3-4 |
-| Token usage | ~40-50k | ~40-50k |
+1. **Quantization acts as a regularizer** — slight weight rounding prevents degenerate thinking loops that BF16 and Q6_K are prone to
+2. **Custom imatrix preserves reasoning weights** — 30% reasoning/self-verification calibration data ensures chain-of-thought and self-correction weights are preserved
+3. **Half the size, better results** — 17GB vs 56GB, fits on a single 24GB GPU
 
-**Test: Build a Quiz Generator & Knowledge Tracker (Python CLI, ~1100 lines)**
+### Full Test Results
 
-| Metric | Splice (this model) | HybridBase |
-|--------|-------------------|------------|
-| Lines of code | 1154 | 837 |
-| Runs without errors | ✅ Yes | ❌ Crashes (NameError) |
-| Looped during generation | No | **Yes, twice** (required intervention) |
-| Feature scope | 8 commands, knowledge tracker | 8 commands, simpler |
-| Token usage | ~80k | Unknown (couldn't complete) |
+**Test 1: Automated coding (5 LeetCode problems, auto-verified)**
 
-### One-Shot Project Tests (Live Coding)
+| Quant | imatrix | Temp 0.6 | Temp 0.8 |
+|-------|---------|----------|----------|
+| **IQ4_NL (custom)** | **reasoning** | **5/5** | **5/5** |
+| Q8_0 (custom) | reasoning | 5/5 | 5/5 |
+| Q6_K (any) | — | 2/5 (loops) | 1/5 (loops) |
+| BF16 | N/A | 2/5 (loops) | 2/5 (loops) |
 
-Two complete projects were given as one-shot prompts to this model — no follow-up corrections, no multi-turn debugging. The model was expected to produce a fully working project from a single detailed prompt, including virtual environment setup, test scripts, and sample data.
+**Test 2: Full-stack project (AI Radar — 4 APIs, frontend, backend, DB, 256k context)**
 
-> **Note:** The author believes the sampling parameters used during testing (temp=0.8, top_p=0.95, top_k=20) may not be fully optimal for this model. There is likely room for improved performance with further parameter tuning.
+| Quant | imatrix | Completed | DB Works | Errors | Loops | Grade |
+|-------|---------|-----------|----------|--------|-------|-------|
+| **IQ4_NL (custom)** | **reasoning** | **✅** | **✅ 175 items** | **3** | **0** | **A-** |
+| Q8_0 (custom) | reasoning | ✅ | ❌ await bug | 2 | 0 | B+ |
+| Q8_0 (wiki) | standard | ✅ | ⚠️ 16 items | 5 | 0 | B |
+| IQ4_NL (wiki) | standard | ❌ | — | 10 | 10+ | F |
+| Q8_0 (BF16 KV) | reasoning | ❌ | — | — | 3+ | F |
 
-**Test 1: Study Flashcard Engine (one-shot, ~40-50k tokens generated)**
+**The custom imatrix is the critical factor** — IQ4_NL-custom (A-) vs IQ4_NL-wiki (F) on identical tasks.
 
-Prompt: Build a complete Python CLI tool with SM-2 spaced repetition, 7 commands, JSON API for agent integration, venv setup, and test suite.
+## Critical: Use F16 KV Cache
 
-| Aspect | Result |
-|--------|--------|
-| Runs without errors | ✅ Yes |
-| All commands functional | ✅ 7/7 |
-| SM-2 algorithm | ✅ Correctly implemented |
-| Project structure | ✅ Proper folder, setup.sh, test.sh, test_notes.txt |
-| Card quality | ✅ Clean term-definition pairs, proper key term extraction |
-| CSV export | ✅ Real CSV output using csv.writer |
-| Deduplication | ✅ Explicit seen_fronts set prevents duplicates |
-| Code style | Clean functional style, 788 lines |
-| Bugs found | 1 minor (case sensitivity in some edge cases) |
+| KV Cache | Mantissa | Long Context Effect |
+|----------|----------|-------------------|
+| **F16 (use this)** | **10 bits** | Stable at 256k |
+| BF16 (avoid) | 7 bits | Loops at 160k+ |
 
-**Test 2: Quiz Generator & Knowledge Tracker (one-shot, ~80k tokens generated)**
+Tested directly: same model, same prompt, 256k context. **F16 KV completed the full project. BF16 KV looped and created 0 files.**
 
-Prompt: Build a more complex Python CLI tool with 8 commands, 4 question types (MCQ, T/F, fill-in-blank, short answer), knowledge tracking with concept-level granularity, study suggestions, progress visualization, and scoring with partial credit.
+Always use `--cache-type-k f16 --cache-type-v f16`.
 
-| Aspect | Result |
-|--------|--------|
-| Runs without errors | ✅ Yes |
-| All commands functional | ✅ 6/8 (suggest command produces generic output with fresh data, weak-topics empty when no errors recorded — both technically correct but not useful as demos) |
-| Question generation | ✅ 4 types working (MCQ, T/F, fill, short answer) |
-| Scoring pipeline | ✅ take → score → knowledge tracker update works end-to-end |
-| Knowledge tracking | ✅ Concept-level accuracy tracking, trend detection |
-| Project structure | ⚠️ Files extracted without containing folder |
-| Token usage | ~80k — the model autonomously managed file creation, venv setup, self-testing, self-review, debugging, and SSH transfer to completion |
-| Bugs found | 2-3 moderate: MCQ questions include markdown headers in question text, T/F generation has a for/else creating unintentional duplicates, suggest command pads with generic entries |
+## Custom Importance Matrix
 
-**Severity assessment of bugs:** All bugs found in Test 2 are **surface-level generation quality issues**, not architectural or logic errors. The scoring pipeline, knowledge tracker, and data persistence all work correctly. The MCQ header parsing and T/F duplicate issues would likely be fixed in a single follow-up prompt ("fix the question text including markdown headers" / "fix the T/F for/else logic"), requiring minimal effort. No bugs required architectural changes.
+The IQ4_NL uses a custom-built imatrix — not standard wiki calibration. This is the single biggest quality factor.
 
-**Note on the 80k generation:** The Quiz Engine was completed entirely autonomously across ~80k tokens with zero human intervention. The model:
-1. Created the full project structure and wrote all code
-2. Set up a Python virtual environment
-3. Ran its own test suite, reviewed the results, and debugged failures
-4. When asked to deliver the project as a zip file — and with no `zip` utility installed on the system — the model independently found an alternative compression method (tar.gz) and used it
-5. Autonomously SSH'd into a separate machine and transferred the compressed project to a specific download folder
+**Calibration dataset (English only):**
 
-The model exhibited this same behavior on the Flashcard Engine test as well — independently compressing and SSH-transferring the completed project without being told how. This appears to be consistent agentic behavior, not a one-off: the model reliably adapts to missing tools and completes delivery steps autonomously across separate sessions.
-
-**Note on testing environment:** All live coding tests were conducted using [OpenCode](https://github.com/opencode-ai/opencode) (a terminal-based AI coding agent) with a standard out-of-the-box configuration — no custom system prompts, no special scaffolding, no agent frameworks. The model was running BF16 (full precision) on an Arch Linux system with root access. The quantized variants (Q8_0 and IQ4_NL) were subsequently tested on 5 automated coding problems at both temperature 0.6 and 0.8 — both scored 5/5, outperforming the BF16 original (see Quantization Quality Testing section above). The imatrix calibration was performed with a custom dataset weighted toward reasoning, self-verification, and code generation.
-
-**Note on alternative approaches:** During development, the author also attempted a more "stable" variant using clean base Qwen3.5-27B weights in the duplicated zone (instead of dnhkng's FP8-origin layers). Ironically, the variant with standard base model layers performed worse on coding tasks — exhibiting looping behavior and variable scoping errors on the same prompts that this Splice model handled cleanly. The dnhkng FP8-origin layers in the reasoning zone appear to contribute meaningfully to coding stability.
-
-### Partial Benchmark Results
-
-**Custom Quick Bench (50 problems: 20 coding, 15 reasoning, 15 math)**
-- Partial results (11/50 completed before timeout): **82% pass rate** (9/11, all coding problems)
-- No looping detected at any point
-
-**IFBench (instruction following, 16/294 completed)**
-- Average response length: 1213 chars
-- 0 looping responses (repetition ratio check)
-- 2 empty responses on very hard constraint prompts
-
-### Expected Performance (Extrapolated from Source Models & dnhkng's Research)
-
-Performance expectations are derived from two sources:
-1. The [official Qwen3.5-27B benchmark scores](https://huggingface.co/Qwen/Qwen3.5-27B)
-2. dnhkng's [LLM Neuroanatomy II](https://dnhkng.github.io/posts/rys-ii/) research, which systematically measured the impact of RYS layer duplication on Qwen3.5-27B using math and EQ (emotional quotient) probe sets
-
-In dnhkng's research, the (26,34) XL configuration achieved a combined delta of **+0.1288** across math and EQ probes with only 12.5% parameter overhead — sitting on the validated Pareto frontier of performance vs. compute cost. The full probe sweep tested all possible (i,j) configurations and identified four optimal variants (S, M, L, XL), with XL (26,34) being the maximum-performance option.
-
-Based on these findings:
-
-| Benchmark | Qwen3.5-27B Official | Expected for this model |
-|-----------|---------------------|------------------------|
-| IFEval | 95.0 | ~90-93 (slight drop from layer modification) |
-| MMLU-Pro | 86.1 | ~83-85 |
-| GPQA Diamond | 85.5 | ~82-84 |
-| LiveCodeBench v6 | 80.7 | ~78-80 |
-| SWE-bench Verified | 72.4 | ~68-71 |
-| HLE w/ CoT | 24.3 | ~22-24 |
+| Content | Weight | Purpose |
+|---------|--------|---------|
+| Reasoning & self-verification | 30% | Math proofs with ✓/✗ checks, debugging narratives, self-correction, algorithm tracing |
+| Code (Python, JS, Bash) | 25% | Multi-file projects, test suites, error handling |
+| Academic papers (broad) | 15% | ArXiv + PubMed across all fields |
+| Instruction/agent prompts | 15% | Terse multi-step commands, agentic style |
+| Infrastructure/sysadmin | 10% | systemd, SSH, GPU config, shell |
+| General English | 5% | Wiki baseline |
 
-*Note: These are estimates based on dnhkng's published RYS research findings and the official Qwen3.5-27B scores. The RYS method was shown to improve reasoning capabilities (math +0.0755, EQ +0.0533 for the XL config in dnhkng's probe sweep) while potentially trading small amounts of instruction-following precision. The uncensored fine-tune may also shift absolute scores. Full standardized benchmark results were not completed due to infrastructure limitations (BF16 thinking mode inference at ~60-120s per request made large benchmark suites impractical). The live coding tests above provide the most reliable quality signal for this model's intended use case.*
+The 30% reasoning allocation preserves the model's ability to self-correct during thinking — the behavior that standard calibration destroys.
 
-## Usage with llama.cpp / ik_llama.cpp
+## Recommended Parameters
 
 ```bash
-# Basic serving
 llama-server \
-  -m RYS-Qwen3.5-27B-Uncensored-Splice-BF16.gguf \
-  --host 0.0.0.0 --port 8000 \
-  -ngl 99 -c 163840 \
+  -m RYS-Qwen3.5-27B-Uncensored-Splice-IQ4_NL.gguf \
+  --host 0.0.0.0 --port 8001 \
+  -ngl 99 -c 262144 \
+  --cache-type-k f16 --cache-type-v f16 \
   --flash-attn on \
   --jinja --reasoning-format deepseek \
   --temp 0.6 --top-p 0.95 --top-k 20 \
   --presence-penalty 0.0 --repeat-penalty 1.0
 ```
 
-**Important server flags:**
-- `--jinja` — required for proper chat template processing
-- `--reasoning-format deepseek` — enables thinking mode (`<think>...</think>`)
-- `--flash-attn on` — recommended for performance
-- Do **NOT** use `--cache-ram` or `--slot-prompt-similarity` — these can cause stale cached responses and model instability
-- Do **NOT** use `--cache-ram-similarity` — known to cause response poisoning between sessions
-
-### VRAM Requirements
+**Do NOT use:** `--cache-type-k bf16`, `--cache-ram-similarity`, `--slot-prompt-similarity`
 
-| Quant | Minimum VRAM (model only) | With 8k context | With 32k context |
-|-------|--------------------------|-----------------|-----------------|
-| BF16 | ~56 GB | ~60 GB | ~72 GB |
-| Q8_0 | ~30 GB | ~34 GB | ~46 GB |
-| IQ4_NL | ~17 GB | ~21 GB | ~33 GB |
+## How It Was Built
 
-*KV cache uses BF16 by default. Add `--cache-type-k bf16 --cache-type-v bf16` for optimal memory usage.*
-
-## Architecture Details
-
-### Qwen3.5 Hybrid SSM
-
-Qwen3.5-27B uses a hybrid architecture combining:
-- **Gated DeltaNet** (linear attention / SSM-style) — 48 of 64 original layers
-- **Full Attention (GQA)** — 16 of 64 original layers, at every 4th position (layers 3,7,11,...63)
-
-The `full_attention_interval=4` pattern is preserved in this 72-layer model:
-- Full attention at layers 3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59, 63, 67, 71
-- All other layers are Gated DeltaNet (linear attention)
-
-### RYS Method Reference
-
-The RYS (Repeat Your Self) method was developed by [dnhkng](https://github.com/dnhkng) and documented in:
-- [LLM Neuroanatomy: How I Topped the LLM Leaderboard Without Changing a Single Weight](https://dnhkng.github.io/posts/rys/)
-- [LLM Neuroanatomy II: Modern LLM Hacking and hints of a Universal Language?](https://dnhkng.github.io/posts/rys-ii/)
-
-The method duplicates specific middle layers of a transformer to enhance reasoning capabilities without any training or weight modification. The (26,34) configuration was identified as the Pareto-optimal "XL" variant through systematic probing with math and EQ (emotional quotient) test sets.
-
-### Layer Map
+### Splice Method
 
 ```
-Output Layer → Source
-0–25         → HauhauCS Uncensored layers 0–25
-26–33        → dnhkng RYS layers 26–33 (base layers 26–33, first pass)
-34–41        → dnhkng RYS layers 34–41 (base layers 26–33, duplicated)
-42–71        → HauhauCS Uncensored layers 34–63
+Layers  0–25:  HauhauCS Uncensored weights (26 layers)
+Layers 26–41:  dnhkng's RYS-XL layers (FP8→F16→BF16, 16 layers = 8 duplicated)
+Layers 42–71:  HauhauCS Uncensored weights (30 layers)
 ```
 
-## Research Foundation
-
-This project would not have been possible without [dnhkng's](https://github.com/dnhkng) groundbreaking RYS (Repeat Your Self) research. The key insights from their work that directly informed this model:
-
-1. **Layer duplication enhances reasoning without training** ([LLM Neuroanatomy](https://dnhkng.github.io/posts/rys/)): dnhkng discovered that duplicating specific middle layers of a transformer creates a "deeper reasoning circuit" that measurably improves performance on math and emotional intelligence benchmarks — with zero training, zero weight modification, and zero data.
-
-2. **Systematic configuration optimization** ([LLM Neuroanatomy II](https://dnhkng.github.io/posts/rys-ii/)): dnhkng conducted exhaustive sweeps of all possible (i,j) duplication configurations on Qwen3.5-27B, measuring each with dedicated math (Math120) and EQ (EQ140) probe sets. This identified four Pareto-optimal configurations:
-   - S (33,34): +1 layer, minimal overhead
-   - M (31,34): +3 layers, balanced
-   - L (30,35): +5 layers, strong
-   - **XL (26,34): +8 layers, maximum performance** ← used in this model
+78% uncensored layers. Built via direct GGUF→GGUF splice — no safetensors conversion.
 
-3. **The hybrid SSM constraint**: dnhkng's work on Qwen3.5's hybrid Gated DeltaNet + Full Attention architecture revealed that the `full_attention_interval=4` pattern must be preserved post-duplication — a critical constraint for GGUF-based inference that is not documented elsewhere.
+### Source Models
 
-4. **Pareto-optimal validation**: The (26,34) configuration was validated not just on the initial scan but on larger probe sets, confirming it as the best performance-per-compute option at +0.1288 combined delta with 12.5% overhead.
+- **[Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B)** — Base architecture (Apache 2.0)
+- **[HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive)** — 78% of layer weights
+- **[dnhkng/RYS-Qwen3.5-27B-FP8-XL](https://huggingface.co/dnhkng/RYS-Qwen3.5-27B-FP8-XL)** — 22% of layer weights (duplicate zone)
+- **[dnhkng/RYS](https://github.com/dnhkng/RYS)** — Method & research ([Blog](https://dnhkng.github.io/posts/rys-ii/))
+- **[ikawrakow/ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)** — Quantization tooling
 
-The author's contribution was to combine these RYS findings with the HauhauCS uncensored fine-tune using the novel splice method — taking dnhkng's already-validated duplicate zone layers directly and embedding them within the uncensored model's layer stack, rather than re-duplicating from scratch.
+## Live Test: AI Radar Project (One-Shot)
 
-## Vision (mmproj) Compatibility
+The model autonomously built a complete AI/ML news aggregator:
+- **26KB FastAPI backend** with 4 live API integrations (GitHub, Reddit, HuggingFace, ArXiv)
+- **25KB dark-theme SPA frontend** (881 lines, search, filters, cards, bookmarks)
+- **SQLite database** with 175 items persisted from live API fetches
+- **Setup & test scripts** — venv, deps, 9 endpoint tests
+- **Self-corrected** 3 tool format errors autonomously
+- **Zero loops** across ~70k token generation at 256k context
 
-Qwen3.5-27B is natively a vision-language model. While this GGUF release is text-only, it should be compatible with the mmproj (multimodal projector) from the uncensored base model. In our testing, the mmproj from [HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive) worked correctly with our model.
+Tested via [claw-code](https://github.com/ultraworkers/claw-code) agent framework (required patching `reasoning_content` support for OpenAI-compatible streaming — see our fork).
 
-We have chosen not to host the mmproj file in this repository as we did not create or modify it — credit for that work belongs entirely to HauhauCS. If you wish to use vision capabilities, download the mmproj directly from their repository.
+Testing environment: [OpenCode](https://github.com/opencode-ai/opencode), Arch Linux, root access, Q8_0-custom with F16 KV cache at 256k context. Temperature 0.6.
 
-## Limitations
+## Vision Compatibility
 
-1. **Uncensored**: This model will follow most instructions without refusal. It is the user's responsibility to ensure ethical use.
-2. **RYS overhead**: 72 layers vs 64 original = ~12.5% more compute per token.
-3. **Thinking mode token usage**: The model generates extensive `<think>` blocks before responding, which increases token usage significantly (often 2-10x the final response length).
-4. **Hybrid architecture**: Requires recent llama.cpp builds (b8000+) that support Qwen3.5's Gated DeltaNet + Full Attention hybrid architecture.
-5. **Not fully benchmarked**: Full benchmark suite was not completed. Performance estimates are extrapolated from source model scores.
-
-## Credits
-
-- **[Qwen Team](https://huggingface.co/Qwen)** — Qwen3.5-27B base architecture and weights
-- **[HauhauCS](https://huggingface.co/HauhauCS)** — Uncensored fine-tune (Qwen3.5-27B-Uncensored-HauhauCS-Aggressive)
-- **[dnhkng](https://github.com/dnhkng)** — RYS method, research, and validated XL configuration
-- **[ikawrakow](https://github.com/ikawrakow)** — ik_llama.cpp (quantization tooling)
+Compatible with mmproj from [HauhauCS](https://huggingface.co/HauhauCS/Qwen3.5-27B-Uncensored-HauhauCS-Aggressive) (tested, not hosted here).
 
 ## Citation
 
-If you use this model, please cite the original works:
-
 ```bibtex
 @misc{qwen3.5,
   title = {{Qwen3.5}: Towards Native Multimodal Agents},
@@ -544,7 +198,7 @@ If you use this model, please cite the original works:
 }
 
 @misc{dnhkng_rys,
-  title = {LLM Neuroanatomy II: Modern LLM Hacking and hints of a Universal Language?},
+  title = {LLM Neuroanatomy II},
   author = {dnhkng},
   year = {2025},
   url = {https://dnhkng.github.io/posts/rys-ii/}