T-Search-APEX-I-MiniPlus-V2.1-GGUF

The Definitive 35B Agentic Retrieval MoE · Multi-Round Search & Evidence Synthesis · Bundled Q8_0 Vision Projector · Full 256K Context on 24GB Workstations

Welcome to APEX-I-MiniPlus-V2.1 for t-tech/T-Search (the specialized 35B Mixture-of-Experts architecture developed by t-tech as an agentic retriever that plans, executes multi-round searches, and synthesizes verifiable evidence chains).

Standard automated community quantizations uniformly degrade sensitive routing matrices and expert feed-forwards down to 2-bit codebooks, corrupting retrieval planning trajectories, hallucinating query filters, and dropping critical visual tokens.

APEX-I-MiniPlus-V2.1 was engineered differently. This is a 100% custom, hand-crafted tensor-by-tensor quantization built with mathematical precision overrides, calibrated importance matrices (imatrix), and an included high-precision Q8_0 multimodal vision projector (mmproj). Whether executing deep research harnesses on an everyday laptop or orchestrating autonomous search agents across 256K context on a 24GB workstation, this release delivers unmatched evidence grounding, zero router drift, and blistering system RAM streaming throughput.

[!TIP]

SYSTEM RAM INFERENCE: FULL OR PARTIAL

This APEX-I-MiniPlus release is designed for full or partial system-RAM inference. Depending on the processor, memory bandwidth, and DDR4/DDR5 configuration, generation can range from 20 to 45 tok/s. With partial GPU offload, systems that cannot fit 128K or more context entirely in VRAM can place the remaining model and context load in system RAM, maintaining stable, responsive generation at longer context lengths.

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 13–14 GB CEILING

This APEX-I-MiniPlus-V2.1 release represents the absolute technological limit of sparse Mixture-of-Experts quantization within the 15–16 GB download envelope. Every single tensor of its 40 layers and 256 micro-experts has been mathematically audited to maximize reasoning precision, eliminate recurrence state drift, and prevent AVX2 CPU dequantization stalls.

[!TIP]

THE QUANTIZATION SWEET SPOT: NEAR-LOSSLESS Q6_K FIDELITY AT 3-BIT FOOTPRINT

Why APEX-I-MiniPlus V2.1 outperforms standard community quants: - Empirical WikiText-2 Perplexity: 5.6716 ± 0.12975 — (ΔPPL ≈ +0.05) from unquantized baseline. - Near-Lossless Q6_K Fidelity at Less-Than-Q3_K_M Weight: Delivers retrieval fidelity and citation consistency typical of Q6_K while occupying 15.23 GB (14.18 GiB) for the main GGUF — 1.47 GB below the 16.7 GB Q3_K_M reference — or 15.84 GB (14.75 GiB) including the bundled projector. - Zero Routing Drift: 100% of expert routing matrices (gate_inp) remain in uncompressed F32, ensuring search queries and evidence tokens are dispatched to the exact right experts on every forward pass.

[!WARNING]

DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!

Regardless of release version, NEVER confuse handcrafted APEX-I-MiniPlus builds with generic community APEX-I-Mini releases: - Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit IQ2_S (dropping below the critical quality floor), leaves the sensitive token output head unarmored at 3-bit Q3_K_M, and compresses attention projections down to Q3_K. In deep retrieval and reasoning agents, this triggers hallucinated search operators, broken syntax brackets, and high perplexity spikes. - Handcrafted APEX-I-MiniPlus (All Editions by IsValorum): Every single MiniPlus release is a custom tensor-by-tensor architecture that preserves uncompressed F32 router gates, armors the token output head in high-precision Q6_K, safeguards attention gates in Q8_0, and keeps core reasoning experts at or above calibrated 3-bit (IQ3_XXS/IQ3_S).

Optimization History & Transparency Notice

We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures:

Quick Navigation & Table of Contents

• The Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Formats
• Empirical Benchmarks & Fidelity Verification
• Model Files & Technical Specifications
• Bundled Q8_0 High-Precision Multimodal Vision Projector
• Surgical Tensor-by-Tensor Quantization Breakdown
• Hardware Throughput & Offload Benchmarks
• The 24GB Miracle: Full 256K Context Runs In VRAM!
• Recommended Configuration & Setup
• Recommended Generation Parameters (t-tech Official)
• Model Inherent Behavior vs. Quantization Fidelity Notice

[!IMPORTANT]

EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP

These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do:

• Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks.
• Best for: General reasoning, agent workflows, tool calling, and flexible multimodal use.
• Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior.
• Best for: Workflows where an abliterated Qwen3.6 variant is explicitly desired.
• Ornith 1.5 APEX-I-MiniPlus-V2.1 — a software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents.
• Best for: Repository-scale development, multi-file code changes, and software-engineering agents.
• Tiel Coder APEX-I-MiniPlus-V2.1 — a specialist coding MoE tuned for agentic programming, iterative tool use, and implementation-heavy work.
• Best for: Focused coding sessions, iterative debugging, and tool-driven implementation.

These remain distinct model families and editions with their own behavior and empirical results. Pick by workload and intended alignment behavior rather than treating them as interchangeable quantization variants.

Empirical Benchmarks & Fidelity Verification

The Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Formats

Where APEX-I-MiniPlus V2.1 sits in the landscape of local quantization formats:

Model Files & Technical Specifications

Bundled Q8_0 High-Precision Multimodal Vision Projector

Standard community uploads often omit the multimodal projector or supply uncompressed FP16 files, bloating memory.

This release includes mmproj-Q8_0.gguf: * Mathematical Precision: Quantized using calibrated Q8_0 with uncompressed F32 normalizations, preserving zero visual artifacting during document inspection, PDF table parsing, and OCR grounding. * Seamless Deployment: Place mmproj-Q8_0.gguf alongside the main model file; llama.cpp and llama-server load it automatically via --mmproj mmproj-Q8_0.gguf.

Surgical Tensor-by-Tensor Quantization Breakdown

Every tensor has been verified directly from the compiled binary weights:

Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)

Empirically verified in Unsloth Studio & llama.cpp:

The 24GB Miracle: Full 256K Context Runs In VRAM!

T-Search APEX-I-MiniPlus-V2.1 fits the entire 256K context window within 24GB VRAM:

Note: Leaves comfortable headroom for display drivers and compute buffers on standard 24GB GPUs (RTX 3090, RTX 4090, RTX 5090).

Recommended Configuration & Setup

1. High-Throughput Server with Built-in MTP Speculative Decoding (llama-server)

T-Search.APEX-I-MiniPlus-V2.1.gguf includes integrated Multi-Token Prediction (MTP) draft layers. To enable ultra-fast self-speculative execution, pass --spec-type draft-mtp:

llama-server.exe \
  -m T-Search.APEX-I-MiniPlus-V2.1.gguf \
  --mmproj mmproj-Q8_0.gguf \
  --spec-type draft-mtp \
  --draft-max 2 \
  --port 8080 \
  --flash-attn on \
  --fit on \
  -c 32768 \
  --cache-type-k q8_0 \
  --cache-type-v q8_0

(Note: Keep --draft-max tight at 1 or 2 for optimal MTP acceptance depth on agentic retrieval trajectories. To run in standard single-stream mode without MTP, simply omit --spec-type draft-mtp).

2. Direct CLI Inference / Agentic Retrieval Harness

llama-cli.exe \
  -m T-Search.APEX-I-MiniPlus-V2.1.gguf \
  --mmproj mmproj-Q8_0.gguf \
  --spec-type draft-mtp \
  --draft-max 2 \
  -c 32768 \
  -ngl 99 \
  --temp 0.60 --top-p 0.95 --top-k 20 \
  -p "<|im_start|>user\nPlan a multi-round retrieval strategy for verifying quantum error correction milestones.<|im_end|>\n<|im_start|>assistant\n"

Recommended Generation Parameters (t-tech Official)

Official generation guidelines specified by t-tech for agentic search retrieval:

Model Inherent Behavior vs. Quantization Fidelity Notice

[!IMPORTANT] Any behavioral nuances, stylistic tendencies, domain-specific search habits, or zero-shot edge-case oversights stem entirely from the original unquantized checkpoint weights and fine-tuning distribution, NOT from the APEX-I quantization process. Handcrafted APEX-I-MiniPlus strictly preserves mathematical tensor fidelity—keeping 100% of expert routing matrices (gate_inp) in uncompressed F32 (zero router drift), armoring the token output head in Q6_K, and safeguarding attention gates in Q8_0. Empirical verification confirms near-zero perplexity loss (ΔPPL ≈ +0.05), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.