jcbtc/Ling-3.0-Flash-CIRU-IU4

Ling 3.0 Flash — CIRU IU4

Ciru builds for Kairic.ai to bring the best inference optimizations to production.

Ling-3.0-Flash-CIRU-IU4 is CIRU's Ling-specific INT4 checkpoint and AMD Strix Halo serving build for InclusionAI's 124B-parameter, 5.1B-active Ling 3.0 Flash model.

Custom runtime required for native IU4: use the ciru/ling3-gfx1151-iu4 branch of ciru-ai/vllm. Stock vLLM can load these weights through W4A16 compatibility, but it does not provide the IU4 path or reproduce the performance figures below.

This release combines three complementary changes:

• Better S4/G32 weights. CIRU recalibrated all 61,440 routed-expert projections in layers 2–41 using a Ling activation-importance objective. The checkpoint remains symmetric signed INT4, group size 32, in the same compressed-tensors format as the official model.
• Native S4 × S4 prefill on AMD IU4. Prefill uses a two-plane residual activation representation and the native gfx1151 integer WMMA lane. The packed checkpoint bytes remain canonical and safe for the normal W4 path.
• Production W4 decode with native MTP K1. Sparse decode stays on the optimized W4 path, where Ling's 512-expert/top-8 routing is better matched to small token batches.

The result is one production profile: native IU4 where it earns throughput, optimized W4 where sparse decode wins, and no user-facing mode selection.

Weight fidelity

Against the unchanged official INT4 checkpoint on CIRU's locked 92 × 4096-token BF16-reference corpus:

Checkpoint	Mean KL	Top-token agreement
Official Ling INT4	0.040795	95.7063%
CIRU IU4 weights	0.035464	95.8784%
Improvement	−13.07%	+0.172 percentage points

The recalibration changed weights, not the model architecture or tensor schema. The final next-token-prediction layer remains byte-identical to the official checkpoint because it was outside the calibration coverage.

Measured serving performance

Validated on one Ryzen AI MAX+ 395 / Radeon 8060S Strix Halo APU with 128 GB unified memory, native Linux, ROCm/HIP 7.15, Python 3.12, Torch 2.13 ROCm, Triton 3.8, and the CIRU gfx1151 vLLM runtime.

Six simultaneous 4K requests

Matched warmed run: six unique uncached 4096-token prompts, 256 forced output tokens each, MTP K1, 16K scheduler budget, prefix reuse disabled.

Metric	Official W4 control	CIRU IU4	Change
Aggregate prefill	504.89 tok/s	644.94 tok/s	+27.74%
Aggregate decode	70.92 tok/s	81.90 tok/s	+15.48%
Median per-request decode	11.92 tok/s	14.51 tok/s	+21.73%
Median TTFT	48.67 s	38.10 s	−21.72%
Batch wall time	70.25 s	55.71 s	−20.69%
MTP acceptance	98.19%	98.32%	+0.13 pp

Both arms completed all requests with zero cached prompt tokens, zero prefix hits, zero preemptions, and no faults.

Full HumanEval and HumanEval+

The production profile completed a six-worker full-suite run with workers replenished continuously:

Evaluation	Result
HumanEval pass@1	159 / 164 — 96.95%
HumanEval+ pass@1	156 / 164 — 95.12%
Aggregate generated throughput	64.01 tok/s
Completion tokens	200,550
Generation wall time	3,133.06 s
MTP acceptance	91.88%
Runtime faults / preemptions	0 / 0

Sampling used InclusionAI's recommended live settings (temperature=0.6, top_p=0.95, top_k=20). The harness imposed a 4096-token ceiling per task; seven tasks reached that ceiling, so these are the measured capped scores rather than an extrapolated score.

Model details

Property	Value
Architecture	BailingMoeV3ForCausalLM / hybrid KDA–MLA MoE
Parameters	124B total, 5.1B active
Experts	512 routed experts, top-8
Native context	262,144 tokens
Weight format	compressed-tensors pack-quantized
Quantization	symmetric signed INT4, group size 32, BF16 scales
Source checkpoint	inclusionAI/Ling-3.0-flash-int4

Usage

The custom runner, gfx1151 kernel sources, extension builder, and production launcher are published in tools/ling_iu4. The branch is pinned to public release commit 40027ba1d.

1. Build the runner

Ubuntu 24.04 / Debian-family setup:

sudo apt update
sudo apt install -y build-essential git libnuma-dev libdrm-dev libelf-dev \
  ninja-build pkg-config python3.12 python3.12-dev python3.12-venv xxd
curl -LsSf https://astral.sh/uv/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"

git clone --branch ciru/ling3-gfx1151-iu4 \
  https://github.com/ciru-ai/vllm.git "$HOME/vllm-ling-ciru-iu4"
cd "$HOME/vllm-ling-ciru-iu4"
git checkout 40027ba1d
bash tools/ling_iu4/install_runtime.sh
source .venv/bin/activate

The installer rebuilds the wheel-pinned ROCr userspace library with the host compiler. This prevents the distributed ROCm 7.15 runtime from leaving a CPU core continuously busy while the gfx1151 server is idle. The launcher checksum-verifies and preloads the corrected library automatically.

Existing installs made from commit 92ccee356 can apply only the idle-runtime hotfix without rebuilding vLLM, the IU4 extension, or the model:

git fetch origin ciru/ling3-gfx1151-iu4
git checkout 40027ba1d
bash tools/ling_iu4/install_rocr_idle_fix.sh "$PWD"

Restart the server afterward. CIRU_DISABLE_ROCR_IDLE_FIX=1 is an emergency diagnostic rollback to the wheel runtime and may restore the idle CPU spin.

2. Download the model

hf download jcbtc/Ling-3.0-Flash-CIRU-IU4 \
  --local-dir "$HOME/models/Ling-3.0-Flash-CIRU-IU4"

3. Launch the production profile

From the CIRU vLLM checkout:

source .venv/bin/activate

MODEL_PATH="$HOME/models/Ling-3.0-Flash-CIRU-IU4" \
  bash tools/ling_iu4/run_ling_ciru_iu4.sh

The launcher provides the measured configuration: native 262,144-token context, six scheduler slots, a 16K batched-token budget, chunked prefill, prefix caching, Ling tool/reasoning parsers, optimized W4 sparse execution, native IU4 execution for occupied expert work, and MTP K1. Override HOST, PORT, MAX_MODEL_LEN, MAX_NUM_SEQS, MAX_NUM_BATCHED_TOKENS, or GPU_MEMORY_UTILIZATION only when needed.

The kernel build and arithmetic smoke were reproduced from the clean public branch on CIRU's Radeon 8060S (gfx1151). The performance measurements were validated on CIRU's native-Linux NixOS host. The Ubuntu commands above are the supported mainstream build path, but are not presented as a separate clean-host benchmark validation. Native Windows vLLM is unsupported; Windows users require WSL2 with AMD ROCDXG.

Request example

curl -fsS http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "Ling-3.0-Flash-CIRU-IU4",
    "messages": [{"role": "user", "content": "Write a Python function that merges overlapping intervals."}],
    "chat_template_kwargs": {"enable_thinking": true},
    "temperature": 0.6,
    "top_p": 0.95,
    "top_k": 20,
    "max_tokens": 1024
  }'

Intended use and limitations

This release targets local, OpenAI-compatible text generation and coding-agent workloads on high-memory AMD Strix Halo systems. It preserves Ling's native reasoning and tool-call behavior.

• The native IU4 acceleration is hardware-specific to gfx1151; other devices use their supported W4A16 kernels.
• The checkpoint is approximately 77 GB before runtime caches and KV cache. A 128 GB unified-memory system is the validated target.
• Benchmark results are single-host measurements and should not be generalized to different ROCm, vLLM, firmware, power, or memory configurations.
• Generated code must be reviewed and sandboxed before production execution.

Credits and license

• Base model, tokenizer, architecture, and official INT4 checkpoint: InclusionAI, MIT license.
• Inference engine: vLLM, Apache-2.0.
• AMD ROCm, HIP, and gfx1151 integer WMMA support: AMD and their respective upstream projects.
• Weight calibration, native IU4 kernels, runtime integration, profiling, and validation: CIRU.

The original MIT license governs the model weights and accompanying model code. Review the upstream model card before deployment.