Advanced Parameters & Tuning
Kernel-level tuning, buffer pool sizing, and thread configuration
1. Hallucination Prevention & Prompt Template Wrapping Guide
In v2.0.1, user-configurable prompt formatting flags resolve conversational divergence and word-salad by ensuring model-specific dialogue boundaries:
--prompt-template chatml: Formats prompt using canonical ChatML tags (<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n). Required for Falcon3-7B Instruct.--prompt-template falcon: Enforces Falcon instruction format. Required for Falcon-E-1B Instruct.--prompt-template raw: Disables wrapping and passes raw text directly. Recommended for BitNet 2B-4T base reasoning.--prompt-prefix <str>&--prompt-suffix <str>: Custom dialogue wrapping prefixes/suffixes.--eos-token-id <int>: Override end-of-sequence token ID to prevent infinite token run-on.
2. Supported Models & Optimal Deployment Matrix
| Model Name | Weights File | Activation | Recommended Template | Recommended GPU Flags |
|---|---|---|---|---|
| Microsoft BitNet 2B-4T | bitnet-2b-ggml-model-i2_s.gguf |
--act-fn relu2 |
--prompt-template raw |
-ngl 30 (Full GPU offload) |
| TII Falcon-E 1B Instruct | falcon-e-1b-instruct-i2_s.gguf |
--act-fn swiglu |
--prompt-template falcon |
-ngl 24 (Full GPU offload) |
| TII Falcon3 7B Instruct | falcon3-7b-instruct-i2_s.gguf |
--act-fn swiglu |
--prompt-template chatml |
--chunk-layers 4 --vocab-slice 32768 |
| BitNet Embedding 270M | bitnet-b1.58-270M-embed-i2_s.gguf |
Linear / RMSNorm | --prompt-template raw |
CPU / Zero-Copy Mmap Vector Search |
3. Mobile GPU Memory Slicing & Watchdog Defense Architecture
To operate 7B scale models reliably across heterogeneous mobile chipsets, v2.0.1 introduces hardware-level execution governance:
- Fence Chunking (
--chunk-layers 4): Splits the 28-layer transformer graph into 4-layer command buffer sub-dispatches with intermediate fence waits. This completely bypasses the ARM Mali 2,500ms kernel watchdog timer on Exynos 1280/1380 devices. - Vocabulary Slicing (
--vocab-slice 32768): Falcon3-7B features a massive 131,080-token FP16 vocabulary matrix consuming 768MB VRAM. By slicing the linear projection to the top 32,768 tokens and masking non-computed logits to-1e9f, VRAM consumption drops to 192MB (-576MB reduction) with zero impact on common vocabulary generation.
4. Mathematical Resolution of Ternary Numerical Collapse
In accordance with technical whitepaper AOSF-TR-2026-BITNET-TERNARY-02, low-level unpacking maps 2-bit unsigned containers to ternary weights using canonical relation:
// Canonical Microsoft BitNet b1.58 dequantization formula:
int8_t w0 = (int8_t)(byte_val & 3) - 1;
int8_t w1 = (int8_t)((byte_val >> 2) & 3) - 1;
int8_t w2 = (int8_t)((byte_val >> 4) & 3) - 1;
int8_t w3 = (int8_t)((byte_val >> 6) & 3) - 1;
// Integrated 32-byte GGUF tensor trailer weight scale:
float total_scale = dequant_scale * weight_scale;
out[r] *= total_scale;5. Upstream Open-Source Contributions & Credibility
Our foundational research directly powers and resolves global open-source issues across Microsoft and GGML:
- microsoft/BitNet #551: Solved ARM QK=128 stride tensor corruption and word salad.
- microsoft/BitNet #624: Completed 1x4_32W parallel NEON sdot acceleration kernel with Android Termux tooling.
- ggml-org/whisper.cpp #4089: Mobile heterogeneous GPU-Encoder / CPU-Decoder split-mode pipeline.
- AOSF-TR-2026-BITNET-TERNARY-02: Official Foundation Research Technical Whitepaper on ARM64 1.58-bit Ternary Numerical Collapse.