Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-Coder-Abliterated

Qwen3.8-Distill-35B-A3B-Coder-Abliterated (Q2 ROCmFPX, PoC)

A 2-bit ROCmFPX GGUF of a distilled Qwen3.6-35B-A3B (MoE, 256 experts / ~3B active), sized to run on a 16GB consumer GPU. Ships with the MTP (nextn) head and build instructions for the matching runtime.

Honest status: this is a proof-of-concept. On the internal 10-task smoke eval the distilled model tied its base (6/10 vs 6/10) — no regression, no measurable gain yet — and it is now quantized to 2-bit, which trades quality for fit. Publishing it as a reproducible artifact of the pipeline (distill → graft MTP → ROCmFPX 2-bit GGUF), not as a benchmark-winning coder. The quality fix is a larger, tool-calling-heavy corpus — a separate follow-up run.

What this is

• —Base / architecture: Qwen/Qwen3.6-35B-A3B (Qwen3_5MoeForCausalLM, 256 experts, ~3B active). The "3.8" in the name refers to the teacher, not the base.
• —Teacher: abliterated Qwen3.8-27B (GGUF Q8_0) via llama.cpp — sequence-level reasoning distillation (teacher <think> chains as SFT targets).
• —Method: Unsloth 4-bit QLoRA, completion_only_loss, 1 epoch / 850 teacher completions, merged to bf16, MTP head grafted back from base, converted + quantized with ROCmFPX.
• —Quant (the interesting part): a hand-built role-aware mix — 2-bit experts (Q2_0_ROCMFPX, the ~90% bulk) + Q6 attention / embeddings / shared-experts / output (Q6_0_ROCMFPX, the coherence-critical ~10%), norms in F32. 12GB total, fits a 16GB card with ~4GB left for KV/context. This is the llama.cpp/ROCmFPX analogue of the eschamoe/OTQ role-aware idea: pure 2-bit-everywhere collapses the model; keeping attention precise while 2-bit'ing the experts preserves coherence. See the exact --tensor-type recipe in BUILD.md.
• —"Abliterated": transferred over the training corpus (teacher was abliterated) — corpus-scoped, NOT a globally abliterated model.

Run it

You need a llama-server built from the pinned ROCmFPX source — see [BUILD.md](BUILD.md).

llama-server -m *-Q2_ROCMFPX.gguf --host 127.0.0.1 --port 8080 \
  -ngl 99 -c 16384 -fa on --jinja --alias qwen38-distill-a3b
# OpenAI-compatible API at http://127.0.0.1:8080/v1

16GB card: context and concurrency share one KV pool — pick single-stream long context (-c 32768 -np 1) or many short sessions (-c 8192 -np 8).

Known limitations (measured)

• —No accuracy gain over base yet; 2-bit lowers quality further.
• —Weak on tool-calling/agentic tasks (thin PoC corpus) — the first thing the next run must fix.
• —MTP nextn tensors are present but speculative decoding depends on your runtime's support (see BUILD.md). Text-only; no vision.

Files

• —*-Q2_ROCMFPX.gguf — the model (~16GB-card fit)
• —BUILD.md — build the ROCmFPX runtime (pinned commit b2f5829)
• —build_rocmfpx.sh — exact build script used

Apache-2.0, inheriting the base model's terms.