WAL-Ternary-8B

English · Русский · Қазақша

WAL-Ternary-8B V77 is an independently constructed low-bit version of Qwen/Qwen3-8B. The complete checkpoint occupies 2.898417 bits per original parameter (BPW). All 252 Transformer-body matrices execute directly from the packed representation; dense Qwen or Neutrino weights are not needed.

Weights and a copy of the runtime are hosted together at armanibadboy/WAL-Ternary-8B.

Research release. CUDA and x86-64 CPU are validated. Apple ARM64/NEON code is included, but physical Apple Silicon validation is still pending.

Representation

W(x) = (alpha · T + beta · R)x + Σ Uᵣ(Vᵣᵀx)
T ∈ {-1, 0, +1}
R = exactly 8 signed residual sites per group of 128 weights

• 252/252 body matrices: T3 + sparse-k8 base.
• 213 matrices: packed binary low-rank WALB2 correction.
• 39 matrices: base only.
• Embedding: symmetric INT3/g128.
• LM head: symmetric INT4/g128.
• Norm and small tensors: BF16.
• Model payload: 2.882034 BPW; complete tree: 2.898417 BPW.

This is a ternary-derived hybrid, not a pure 1.58-bit BitNet checkpoint.

Verified results

Frozen original gate	Required	V77
Complete checkpoint	≤2.952031 BPW	2.898417 BPW
C4 perplexity	≤22.5637	20.1743
MMLU-Redux, 5-shot	≥57.28%	59.29%
GSM8K strict	≥58.66%	76.57%

Additional reference results: GSM8K flexible 77.10%, IFEval strict 51.57%, IFEval loose 54.90%.

Direct-packed parity:

Metric	Materialized reference	Direct packed
C4 PPL	20.1743	20.2156
MMLU-Redux	59.29%	59.26%
GSM8K strict	76.57%	76.19%
GSM8K flexible	77.10%	76.35%

Runtime

The runtime retains no dense INT8/BF16 Transformer body.

Backend	Result	Memory	Status
H200, CUDA V18 Graph, 2048 KV	72.41 tok/s	3.93 GB	validated
H200, auto 256 KV	99.36 tok/s	3.66 GB	validated short-context run
Xeon CPU V4, 64 threads	5.93 tok/s	4.12 GB RSS	validated
Apple ARM64/NEON	—	—	implemented; Mac validation pending

These are hardware-specific measurements, not universal guarantees.

Install

From GitHub:

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install 'git+https://github.com/AubakirovArman/WAL-Ternary-8B.git#egg=wal-tat[runtime]'
wal-runtime doctor

From the copy bundled with the model:

git clone https://huggingface.co/armanibadboy/WAL-Ternary-8B
cd WAL-Ternary-8B/runtime
python -m pip install '.[runtime]'
wal-runtime doctor

Build the approximately 2.3 GB executable cache:

wal-runtime prepare armanibadboy/WAL-Ternary-8B \
  --output ~/.cache/wal/WAL-Ternary-8B-v77

The cache stores packed ternary symbols, sparse positions/signs and FP16 scales—not a reconstructed dense weight tree. Parent, ABI, converter, per-matrix lineage and SHA-256 hashes are verified fail-closed.

Generate on NVIDIA:

wal-runtime generate armanibadboy/WAL-Ternary-8B \
  --backend cuda-v18 \
  --hardware-cache ~/.cache/wal/WAL-Ternary-8B-v77 \
  --prompt 'Explain why the sky is blue in one sentence.' \
  --max-new-tokens 256

Generate on CPU:

wal-runtime generate armanibadboy/WAL-Ternary-8B \
  --backend cpu-v4 --device cpu \
  --hardware-cache ~/.cache/wal/WAL-Ternary-8B-v77 \
  --threads 16 --prompt 'Explain photosynthesis.'

Benchmark:

wal-runtime benchmark armanibadboy/WAL-Ternary-8B \
  --backend cuda-v18 \
  --hardware-cache ~/.cache/wal/WAL-Ternary-8B-v77 --tokens 128

The first CUDA run JIT-compiles the included kernels and needs CUDA/NVCC and Ninja. CPU needs a C++17 compiler. Apple Silicon needs native arm64 Python, PyTorch and Xcode Command Line Tools; use cpu-v4. The portable MPS fallback is --backend portable --device mps, not a fused Metal backend.

Complete guides:

• English
• Русский
• Қазақша

CLI

wal-runtime doctor
wal-runtime inspect MODEL
wal-runtime prepare MODEL --output CACHE
wal-runtime generate MODEL --hardware-cache CACHE --prompt TEXT
wal-runtime benchmark MODEL --hardware-cache CACHE --tokens 128

Repository

src/wal_tat/runtime/       CLI, validation and packed backends
src/wal_tat/runtime/_v18/  CUDA V18, CPU V4 and reference modules
src/wal_tat/runtime/experiments/cuda/ CUDA C++ kernels
tests/                     focused runtime tests
docs/                      format, benchmarks, guides and article

Limitations

• Research runtime, not yet a production serving engine.
• First-run JIT; precompiled wheels are not yet published.
• CPU prefill still needs batched packed GEMM.
• Apple Silicon and non-SM90 CUDA validation are incomplete.
• IFEval remains numerically sensitive; see benchmarks.
• Coding, tool use, safety, multilingual and long-context testing is limited.

The full history is in the Russian research article and English technical paper.

License

Apache-2.0, following the upstream Qwen/Qwen3-8B license. No Neutrino tensor is included. This project is independent of Alibaba/Qwen and Fermion Research.

Built by Arman Aubakirov with an AI-assisted experimental workflow.