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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.
Qwen/Qwen3.6-35B-A3B (Qwen3_5MoeForCausalLM, 256 experts, ~3B
active). The "3.8" in the name refers to the teacher, not the base.Qwen3.8-27B (GGUF Q8_0) via llama.cpp — sequence-level reasoning
distillation (teacher <think> chains as SFT targets).completion_only_loss, 1 epoch / 850 teacher completions,
merged to bf16, MTP head grafted back from base, converted + quantized with ROCmFPX.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.llama-server built from the pinned ROCmFPX source — see BUILD.md.1llama-server -m *-Q2_ROCMFPX.gguf --host 127.0.0.1 --port 8080 \
2 -ngl 99 -c 16384 -fa on --jinja --alias qwen38-distill-a3b
3# OpenAI-compatible API at http://127.0.0.1:8080/v1-c 32768 -np 1) or many short sessions (-c 8192 -np 8).nextn tensors are present but speculative decoding depends on your runtime's support
(see BUILD.md). Text-only; no vision.*-Q2_ROCMFPX.gguf — the model (~16GB-card fit)BUILD.md — build the ROCmFPX runtime (pinned commit b2f5829)build_rocmfpx.sh — exact build script used