README.md

metadata

license: apache-2.0
base_model:
  - Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF
base_model_relation: quantized
quantized_by: xero0000
pipeline_tag: text-generation
library_name: gguf
tags:
  - gguf
  - qwen35moe
  - moe
  - mixed-precision
  - imatrix
  - ik_llama.cpp
  - coding
  - agentic
  - q2_k

🔬 Kleiner — Qwopus-3.6-35B-A3B-Coder Mixed q2_K + imatrix

Black Mesa mixed-quant series · the coder/engineer.

A CPU-offload-aware mixed-precision GGUF of Qwopus-3.6-35B-A3B-Coder — an agentic-coding fine-tune of Qwen3.6-35B-A3B (Opus-flavored, thinking-off). Runs the full 256K context at ~74 tok/s decode on a single 18 GB dual-GPU desktop, by quantizing the CPU-offloaded expert layers to Q2_K while keeping the GPU-resident tensors at Q4_K.

TL;DR: a 19 GB file that runs a strong coding model at full 256K context, at small-context speed — the fast daily driver for agentic/tool-use coding.

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What it is

Jackrong's Qwopus-3.6-35B-A3B-Coder is Qwen3.6-35B-A3B (hybrid qwen35moe: gated attention + gated-delta-net SSM, 256 experts, 8+1 active, ~3B active/token) fine-tuned for agentic coding — repository tasks, debugging traces, tool schemas, multi-turn feedback — with thinking-off behavior to cut token waste in agent loops. It reports SWE-bench 62.4% (thinking off) and, per its card, beats Ornith-1.0 on legit-request compliance and multi-turn orchestration.

This build applies the mixed q2_K + imatrix quantization so you get that coding model at the fast 256K profile on limited VRAM.

Recipe

• Base: Qwopus-3.6-35B-A3B-Coder (qwen35moe, 40 blocks + 1 nextn/MTP layer).
• Source → output: requantized from the Q8_0 with an importance matrix computed on the Q8 itself (~61K tokens), q2_K on the offloaded expert layers, q4_K GPU-resident, q6_K output.
• Mixed layout:
  • ffn_*_exps on blocks 13–26 → Q2_K (42 tensors — the CPU-offloaded set)
  • everything else → Q4_K · output-class → Q6_K
• 4.88 bpw effective, ~19 GB, 256K native context.

See the Gordon (base Qwen3.6) card for the full rationale on why offloaded-layer byte count (not file size) drives decode speed.

Benchmarks

RTX 3060 Ti (8 GB) + RTX 3080 (10 GB), Ryzen 5950X, 46 GB DDR4-2733, ik_llama.cpp, q4_0 KV, flash-attn on:

Metric	This mixed q2_K	Qwopus Q8_0
Decode	~74 tok/s @256K	~32 tok/s @64K
Context	262144	65536
VRAM	~16.8 GB	15.6 GB (+29 GB RAM)
Output	clean code, --reasoning off	clean

How to run (ik_llama.cpp)

The -ot override is required (pins the Q2_K layers 13–26 to CPU). --reasoning off matches the model's thinking-off design (snappy agent loops, direct code).

./llama-server \
  -m Qwopus3.6-35B-A3B-Coder-mixed-q2k.gguf \
  --jinja --cache-type-k q4_0 --cache-type-v q4_0 --flash-attn on \
  --ctx-size 262144 --parallel 1 --n-gpu-layers 99 \
  -ot 'blk\.(1[3-9]|2[0-6])\.ffn_(up|gate|down)_exps\.weight=CPU' \
  --tensor-split 44,56 --ubatch-size 256 \
  --reasoning off --reasoning-budget 0 \
  --no-mmap --threads 8 --no-warmup --port 8000

A note on the MTP head

The base has a built-in MTP (multi-token-prediction) layer (blk.40.nextn.*) claiming 1.4–2.2× faster generation. That speedup needs vLLM/SGLang — in llama.cpp/ik_llama the MTP head is ignored and the model runs as a normal A3B (ik_llama's MTP support is gated to the gemma4 arch, not qwen35moe). The tensor is harmlessly carried in this quant.

Intended use & limitations

• Target: local agentic/tool-use coding at full 256K on ~16–18 GB VRAM, at usable speed.
• The Q2_K expert layers are the quality floor; for maximum fidelity use the Q8_0.
• Inherits the capabilities and biases of the base Qwopus-Coder. Pure quantization — no fine-tuning or alignment changes.

Provenance

• Original base: Qwen3.6-35B-A3B by Qwen (Apache-2.0).
• Coding fine-tune: Jackrong (Qwopus-3.6-35B-A3B-Coder).
• Mixed quantization + imatrix + tuning: xero0000, June 2026.

Released under the base model's Apache-2.0 license (quantization does not change the license).