---

license: apache-2.0

base_model: Qwen/Qwen3.8-27B

tags:

- gguf

- qwen3.8

- rys

- layer-surgery

- reasoning

- bfcl

- function-calling

language:

- en

---

Qwen3.8-27B — RYS Layer Surgery (GGUF)

An experimental modification of

Qwen3.8-27B, produced by RYS layer

duplication: no training and no weight changes, just running language layers

16–19 (zero-based) a second time during the forward pass.

The source quant is

unsloth/Qwen3.8-27B-GGUF,

revision f1bfb127c64f7072bdd2cad55f258b9c8b2910fe. The method is based on

David Ng's RYS work.

TL;DR

This variant is intended for low-effort reasoning mode. It improved a small

reasoning probe by +11.76 percentage points and a fixed 1,006-case BFCL v4

single-turn evaluation by +0.60 pp micro accuracy. The BFCL gain is small

and not statistically decisive (36 RYS-only wins versus 30 base-only wins,

two-sided exact McNemar p = 0.539), so treat this as an experimental rather

than universally superior release.

The larger EQ-140 result was effectively flat-to-slightly-down (−0.99). Math

also regressed in the search probe; math was recorded as a diagnostic, not used

as a release gate, because it is an expected and fine-tunable tradeoff here.

| Evaluation | Base | RYS 16–19 | Change |

|---|---:|---:|---:|

| Reasoning probe (17 prompts) | 58.82% | 70.59% | +11.76 pp |

| EQ-140 (139 scored) | 82.65 | 81.66 | −0.99 |

| BFCL micro accuracy (1,006 cases) | 82.60% | 83.20% | +0.60 pp |

| BFCL category macro mean | 80.54% | 81.81% | +1.28 pp |

| Math probe (diagnostic only) | 1.000 | 0.750 | −0.250 |

File

| File | Quant | Language layers | Size | SHA-256 |

|---|---|---:|---:|---|

| Qwen3.8-27B-rys_16-19-UD-Q4_K_XL.gguf | UD-Q4_K_XL | 68 | 18.9 GB | a3013e582490a386d4b8fafbaad07bfec65e7727fb5dcf992914babdc2d65ed4 |

The terminal MTP block from the source GGUF is retained and shifted after the

four inserted language layers.

BFCL results

The evaluation used a fixed 1,006-case manifest spanning 13 BFCL v4

single-turn categories. Categories with at least 100 entries used 100 cases;

smaller categories used all available entries. This is not the current full

3,641-case BFCL suite and excludes multi-turn, memory, and web-search tasks.

| Category | N | Base | RYS 16–19 | Change |

|---|---:|---:|---:|---:|

| irrelevance | 100 | 86.00% | 88.00% | +2.00 |

| multiple | 100 | 94.00% | 91.00% | −3.00 |

| parallel | 100 | 90.00% | 92.00% | +2.00 |

| parallel_multiple | 100 | 82.00% | 84.00% | +2.00 |

| simple_java | 100 | 54.00% | 53.00% | −1.00 |

| simple_javascript | 50 | 64.00% | 62.00% | −2.00 |

| simple_python | 100 | 94.00% | 94.00% | 0.00 |

| live_irrelevance | 100 | 95.00% | 99.00% | +4.00 |

| live_multiple | 100 | 79.00% | 77.00% | −2.00 |

| live_parallel | 16 | 87.50% | 87.50% | 0.00 |

| live_parallel_multiple | 24 | 62.50% | 70.83% | +8.33 |

| live_relevance | 16 | 75.00% | 81.25% | +6.25 |

| live_simple | 100 | 84.00% | 84.00% | 0.00 |

| Micro accuracy | 1,006 | 82.60% | 83.20% | +0.60 |

| Macro mean | 13 categories | 80.54% | 81.81% | +1.28 |

Inference used llama.cpp's OpenAI-compatible chat-completions endpoint with

native tool use, the embedded Unsloth chat template, four slots, Q8 KV cache,

and these matched settings for both models:

reasoning_effort = low
enable_thinking  = true
preserve_thinking = true
temperature = 1.0
top_p       = 0.95
top_k       = 20
min_p       = 0.0
seed        = 3407
max_tokens  = 2048

The RYS pass generated in 59:16. Normalizing the clean 991-case portion of the

resumed base pass to 1,006 cases gives about 59:41, so wall time was effectively

flat in this run. RYS produced about 2.0% more output tokens.

What changed?

Qwen3.8-27B uses a repeating three-DeltaNet/one-full-attention hybrid. RYS

insertions therefore need to preserve four-layer blocks. This model duplicates

the half-open layer window [16, 20):

Base:  0 → … → 15 → 16 → 17 → 18 → 19 → 20 → … → 63 → MTP
RYS:   0 → … → 15 → 16 → 17 → 18 → 19
                     → 16 → 17 → 18 → 19 → 20 → … → 63 → MTP

The sweep covered every compatible four-layer window at stride four, followed

by neighboring starts around the strongest reasoning regions. Layers 16–19

were selected because they gave the best reasoning-first result while keeping

the small EQ change near zero. The full BFCL result points in the same positive

direction, though modestly.

Usage

Use a recent llama.cpp build. Start the server with thinking support and the

embedded Jinja template:

./build/bin/llama-server \
  -m Qwen3.8-27B-rys_16-19-UD-Q4_K_XL.gguf \
  --port 8081 -np 4 -ngl all \
  -b 2048 -ub 1024 --flash-attn on \
  --jinja --no-host --reasoning on --reasoning-preserve \
  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0

Request low-effort reasoning explicitly:

{
  "model": "qwen3.8-27b-rys",
  "messages": [{"role": "user", "content": "Your prompt"}],
  "temperature": 1.0,
  "top_p": 0.95,
  "top_k": 20,
  "min_p": 0.0,
  "chat_template_kwargs": {
    "enable_thinking": true,
    "reasoning_effort": "low",
    "preserve_thinking": true
  }
}

The embedded Unsloth template already supports low-effort and reasoning-off

modes. A quick comparison with froggeric/Qwen-Fixed-Chat-Templates v22 found

no material reasoning-quality difference, so this GGUF retains the source

template.

When to use this

• You run Qwen3.8 with low-effort reasoning enabled.
• You value the directional gains on parallel/relevance tool-use tasks.
• You are comfortable evaluating an experimental layer-surgery variant for

your own workload.

When not to use this

• You need a proven across-the-board improvement; the aggregate BFCL gain is

small and not statistically decisive.

• EQ or math performance must not regress at all.
• Your workload is dominated by multiple, live_multiple, or simple

JavaScript function calling, where the base model did slightly better here.

Credits

• Qwen for Qwen3.8-27B
• Unsloth for the source

UD-Q4_K_XL quant

• David Ng for the RYS method
• llama.cpp for local inference
• Berkeley Function-Call Leaderboard

for the evaluation harness

License

Apache 2.0, inherited from Qwen3.8-27B and the source GGUF.