README.md

---
license: apache-2.0
language:
- en
- zh
- multilingual
tags:
- gguf
- qwen3
- qwen3.6
- reasoning
- coding
- academic-writing
- uncensored
- rys
base_model:
- Qwen/Qwen3.6-27B
---

# Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL GGUF

Hyper-focused Q4NL RYS release for:
- programming
- technical reasoning
- academic-style writing

This release is built from:
- AEON source model:
  `https://huggingface.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored`

Use this model with:
- custom `ik-llama` fork, specialized and tuned for this exact model:
  `https://github.com/noonr48/qwen36-aeon-ik-llama`

Side note (tool calling):
some prompts can trigger repeated *identical* tool calls in one assistant turn (especially when the tool result is empty / slow).
Update to the latest `ik-llama` fork version: it now deduplicates identical `tool_calls` server-side.

## At a glance

- released Q4_NL GGUF:
  [`Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL-ik-llama-custom-mixed.gguf`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/blob/main/Qwen3.6-27B-AEON-RYS-MaxThinkCoder-IQ4_NL-ik-llama-custom-mixed.gguf)
- BF16 GGUF reference:
  [`Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/blob/main/Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf)
- HF-format BF16 safetensors:
  [`bf16-safetensors/`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/tree/main/bf16-safetensors)
- intended runtime:
  custom `ik-llama`
- compression:
  `54G` BF16 -> `16G` IQ4_NL
- mixed validation snapshot:
  `0.7299` BF16 -> `0.7244` IQ4_NL
- overall performance change:
  `-0.0055` absolute, about `-0.75%` relative
- public codename:
  `MaxThinkCoder`
- project focus:
  get as much capability as possible out of a Q4-class RYS model for hard English-first work
- not a generic chat release
- not a stock `llama.cpp` release



## BF16 GGUF

A BF16 GGUF is also included for people who want the unquantized GGUF-side artifact from the same released `15,20` RYS branch:

[`Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/blob/main/Qwen3.6-27B-AEON-RYS-MaxThinkCoder-BF16.gguf)

Use this if you want a GGUF reference build, local conversion/testing, or to compare quantization behavior against the released `IQ4_NL` GGUF. For normal inference, the `IQ4_NL` file is the practical target. For Transformers/LoRA/SFT workflows, use the [`bf16-safetensors/`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/tree/main/bf16-safetensors) folder instead.

Size note:
- BF16 GGUF: about `54G`
- IQ4_NL GGUF: about `16G`

## BF16 vs released custom IQ4_NL

This is the critical compression result for the released custom `15,20` branch:
- BF16 size:
  `54G`
- released IQ4_NL size:
  `16G`
- mixed 4-probe mean:
  `0.7299` BF16 -> `0.7244` IQ4_NL
- net performance change:
  `-0.0055` absolute, about `-0.75%` relative

Probe-level snapshot:

| probe | BF16 | IQ4_NL |
|---|---:|---:|
| `math_16` | `0.8421` | `0.7897` |
| `eq_16` | `0.7123` | `0.7111` |
| `math_4` | `0.4851` | `0.5170` |
| `gsm8k_5` | `0.8800` | `0.8800` |

Practical read:
- the released Q4 model is roughly `70%` smaller on disk
- the mixed validation snapshot stayed under a `1%` overall drop
- `eq_16` and `gsm8k_5` were effectively flat
- `math_4` did not regress in this tiny probe
- the real measurable hit was mostly on `math_16`

## Speed snapshot

Exact comparison hardware:
- `6x NVIDIA GeForce RTX 5060 Ti`

| runtime | tested file | ctx | np | KV | decode tok/s | prompt tok/s | note |
|---|---|---:|---:|---|---:|---:|---|
| patched upstream-style `llama.cpp` | same internal standard-typed comparison file | `4096` | `1` | `f16` | `22.51` | `187.18` | internal comparison only |
| custom `ik-llama` fork | released custom-mixed file | `409600` | `2` | `f32/f32` | `39.37` | `164.98` | actual deployment target |

## Why there is no `llama.cpp` file in this release

We did build and benchmark an internal standard-typed comparison artifact.
We are not releasing it as a public `llama.cpp` file.

Why:
- the main model this project is about is the custom mixed GGUF, which needs the forked `ik-llama` runtime
- even the internal standard-typed path was only validated on a patched upstream-style `llama.cpp`, not clean stock mainline
- since users would still need a special runtime path anyway, we did not think it was worth shipping a second public file that suggests plain stock `llama.cpp` support

So the intended reading is simple:
- this repo releases the `ik-llama`-targeted model
- if you want plain stock `llama.cpp`, this is not that release

## Hyper-focused project

This was a deliberately narrow project.

The target was not “best general chat model”.
The target was:
- strongest Q4-class English-first model we could get for coding, reasoning, and academic work
- using the AEON uncensored branch as the source
- using the custom `ik-llama` path because prior RYS experiments suggested that path preserved quality better than standard `llama.cpp`-style quantization

## Imatrix calibration profile

The quantization was deliberately biased toward reasoning and technical work.

Heuristic calibration breakdown:
- `math_reasoning`: `5,688` chunks, `1,706,070` chars (`36.0%`)
- `code_technical`: `3,518` chunks, `1,343,392` chars (`28.4%`)
- `experiment_docs`: `808` chunks, `224,169` chars (`4.7%`)
- `writing_chat`: `387` chunks, `164,097` chars (`3.5%`)
- `other`: `5,139` chunks, `1,249,396` chars (`26.4%`)

Practical read:
- heavy focus on reasoning math, code, technical prose, and experiment artifacts
- very little emphasis on generic social chat

## RYS choice

This release came from the AEON-derived `15,20` RYS branch.
That was the practical release target because it quantized cleanly and held up as the best balanced candidate for this experiment.

## Use case

Recommended:
- coding
- technical reasoning
- academic-style writing
- long-context English work

Not recommended as a generic safe-default chat model.
This branch came from an uncensored source path.


## BF16 safetensors for fine-tuning

The original HF-format BF16 checkpoint for the released `15,20` RYS branch is included here:

[`bf16-safetensors/`](https://huggingface.co/jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF/tree/main/bf16-safetensors)

Use the files in that folder for Transformers-based work such as LoRA, SFT, continued training, or conversion into another training format. Use the GGUF file in the repo root for `ik-llama` inference.

Loading example:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "jackasda211233/Qwen3.6-27B-AEON-RYS-15-20-GGUF"
subfolder = "bf16-safetensors"

tok = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    subfolder=subfolder,
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)
```

RYS note for anyone rebuilding or modifying this checkpoint: the config is part of the model. The BF16 folder keeps the corrected hybrid-stack metadata for the `15,20` insert, including `text_config.num_hidden_layers = 69` and a 69-entry `text_config.layer_types` list. Do not change the layer count without remapping `layer_types` to the same layer order as the tensors.