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Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.
- Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
- Model Files & Technical Specifications
- Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
- Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)
- Estimated Projections on Consumer Hardware
- The 24GB Miracle: Full 256K Context Runs In VRAM!
- Hardware Throughput Projections (RTX 30 / 40 / 50)
- Recommended Generation Parameters (Kwaipilot Official)
- 7. Community Compute Fund & Student Support
[!NOTE]
ARCHITECTURE SELECTION GUIDE — MINIPLUS V2 & V2.1 EDITIONS
This repository hosts the MiniPlus V2 edition of KAT-Coder-V2.5-Dev. Our releases are precision-engineered for specific hardware budgets and memory topologies. V2 is NOT obsolete or "worse"; each edition serves distinct inference requirements:
- MiniPlus V2 (High Theoretical Layer Protection): On paper, V2 provides extra protective envelopes on edge layers (10 layers in
IQ3_S+IQ4_NLshared experts +Q8_0attention gates). However, in practical inference benchmarks—even across extreme long-context windows exceeding +160K tokens—there is virtually NO perceptible difference in quality or reasoning compared to V2.1.- MiniPlus V2.1 (System RAM Streaming Specialist with Deep Context): Specially prepared to run totally or partially in system RAM (DDR4/DDR5) across large codebase contexts (up to 256k tokens). By replacing non-linear codebooks with linear
Q3_Kedge experts, keepingQ8_0attention gates, and upgrading shared foundation experts toQ5_Kacross all 40 layers, it completely eliminates AVX2 CPU dequantization stalls (+24 to 28+ tok/s streaming). Depending on your processor and memory bandwidth (DDR4/DDR5), streaming generation in system RAM can be almost as fast as having everything in VRAM, while supporting deep context reserving GPU VRAM for the codebase KV cache while model weights stream from system RAM. It provides this massive RAM streaming acceleration for only approx. 100 MB more, which is completely negligible in system RAM.Which one should you choose? (Official Recommendation: V2.1)
- ⭐ PRIMARY RECOMMENDATION — KAT-Coder-V2.5-Dev APEX-I-MiniPlus V2.1: For virtually all users and deployments, V2.1 is the strictly recommended release. Empirically verified on WikiText-2, V2.1 achieves an outstanding Perplexity of 5.5045 ± 0.1330 (ΔPPL ≈ +0.06 from unquantized baseline (approx. 5.44)), matching the token fidelity of Q5_K / Q6_K class quantizations while weighing only approx. 14.7 GB (same footprint as Q3_K_M). Furthermore, it completely eliminates AVX2 CPU stalls, providing blistering +24 to 28+ tok/s streaming under system RAM offload.
- MiniPlus V2 Legacy: Maintained for architectural transparency and users seeking specialized configurations for their workflow.
Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases. To explore or download the V2.1 edition of KAT-Coder-V2.5-Dev optimized for system RAM streaming, visit: IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF
[!WARNING]
DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!
Regardless of release version (whether V1, V2, or V2.1), 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-bitQ3_K_M, and compresses attention projections down toQ3_K. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.- Handcrafted APEX-I-MiniPlus (All Editions by IsValorum): Every single MiniPlus release—from V1 and V2 to V2.1—is a custom tensor-by-tensor architecture that preserves uncompressed
F32router gates, armors the token output head in high-precisionQ6_K, safeguards attention gates inQ8_0, and keeps core reasoning experts at or above calibrated 3-bit (IQ3_XXS/IQ3_S). Even our earlier builds vastly outperform generic community APEX recipes and flat 3-bit quants.
[!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]
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.
🏅 Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
[!NOTE] External report: zephel01 independently benchmarked Occamy V2. The benchmark below was performed on Occamy-1.0 APEX-I-MiniPlus V2, not on this specific model. It is included as independent evidence of the broader MiniPlus quantization approach.
The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q8_0 -ctv q8_0 -ngl 99).
The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench):
- L6 Multi-File Code Generation (60 tasks):
- Context 32,768 (32K): 93.3% Resolved (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
- Context 65,536 (65K): 90.0% Resolved (45/60 tasks passed 5/5 consecutive trials).
- Match with 25–28 GB Models: Matches or exceeds the resolution rate of full 25–28 GB models (such as
Ornith-1.5andTiel-Coder35B-A3B) while consuming over 10 GB less VRAM (14.6 GB vs approx. 26 GB).
- Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):
- 32K Context: 14.6 GB total VRAM allocation.
- 65K Context: 15.1 GB total VRAM allocation (only +0.5 GB VRAM added when doubling context!).
- Architectural Explanation: Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM ($O(1)$ constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that 65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080) without offloading to system RAM.
- Measured Real-World Throughput: Sustained single-stream generation of approx. 247 – 251 tok/s on NVIDIA RTX 5090.
Model Files & Technical Specifications
| File Name | File Size | Memory Footprint | BPW | Description |
|---|---|---|---|---|
KAT-Coder-V2.5-Dev.APEX-I-MiniPlus-V2.gguf | 14.64 GB (13.64 GiB) | 13.64 GiB | 3.38 BPW | Core agentic code synthesis, syntax verification, refactoring & logic |
- Base Architecture:
Qwen3_5MoeForConditionalGeneration(40 layers, 256 fine-grained micro-experts with intermediate dimension 512, 8 active per token). - Active Parameters: approx. 3.2B active parameters per token (delivering small-model throughput with 35B-scale reasoning).
- Quantization Profile: Armored boundary layers (
IQ3_S/IQ4_NL), deep core expert compression (IQ3_XXS+imatrix), uncompressed router gates (F32), and high-precision syntax output head (Q6_K). - Memory Footprint: Ultracompact 13.64 GiB footprint engineered specifically to avoid Out-Of-Memory (OOM) crashes on 16GB and 24GB VRAM hardware.
Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
Also, don't confuse APEX-I-MiniPlus-V2 with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit IQ2_S and leaves output.weight at 3-bit Q3_K_M, which creates a noticeable perplexity hit on complex reasoning tasks. V2 was specifically re-engineered to avoid that quality floor (keeping core experts at calibrated IQ3_XXS, output in Q6_K, shared expert in non-linear IQ4_NL, and routers in F32).
To put the numbers in perspective: this cuts nearly 2 GB off a flat 3-bit quant (approx. 15.6 GB), and weighs only about approx. 1 GB more than a generic APEX-I-Mini (approx. 12.5 GB). For that single extra gigabyte of VRAM, you get a massive jump in reasoning and syntactic stability.
Take a look at the tensor-by-tensor comparison table below to inspect the exact architectural differences and see why this specific allocation is optimal. That's specifically what this was built for:
| Architectural Component | Generic Automated Quants (Flat Q3_K_S / IQ3_S) | Generic APEX-I-Mini (Baseline Recipe) | Our Handcrafted APEX-I-MiniPlus-V2 (IsValorum) | Perceived Quality & Real-World Impact |
|---|---|---|---|---|
Output Head (output.weight) | Flat IQ3_S / Q3_K_S (approx. 3.44 BPW) | Inherits base type Q3_K_M (approx. 3.44 BPW unarmored) | Q6_K (approx. 6.56 BPW uncompromised) | Eliminates Syntax & Vocabulary Hallucinations: Low-bit output heads cause tokenizer classification noise, breaking code indentation, brackets ({}, []), math symbols, and domain terms. Q6_K preserves near-FP16 output classification. |
Expert Routers (ffn_gate_inp.weight) | Blindly quantized to 3-bit / unoptimized | Inherits base type Q3_K_M (approx. 3.44 BPW compressed) | F32 uncompressed (32.0 BPW, 2 MB/layer) | Zero Router Drift: In micro-expert models, even minuscule quantization errors in router logits misdirect tokens to wrong experts. Retaining uncompressed F32 guarantees 100% routing fidelity with virtually zero memory overhead (approx. 80 MB total). |
Attention & Language (attn_output, attn_qkv) | Flat IQ3_S / Q3_K_S | Q3_K on 34 middle layers (L3–36), Q4_K on 6 edge layers | Q6_K for attn_output, IQ3_S for attn_qkv | Contextual Retrieval Precision: Generic APEX reduces attention and language projections to Q3_K across 85% of layers. Our V2 build protects attention output in high-precision Q6_K and uses calibrated non-linear IQ3_S, ensuring flawless needle-in-a-haystack retrieval across deep 128k–256k context windows. |
Attention Gates (attn_gate.weight) | Blindly compressed to 3-bit | Compressed to Q3_K (middle) / Q4_K (edges) | Q8_0 (8.50 BPW) | Attention Head Stability: Attention gates modulate query-key routing across hybrid attention layers. Keeping them in 8-bit prevents attention crosstalk and hallucination over long contexts. |
Shared Foundation Expert (ffn_*_shexp) | Flat IQ3_S / Q3_K_S (3.44 BPW) | Linear Q4_K (middle) / Q5_K (edges) | IQ4_NL (4.50 BPW non-linear codebook) | Foundational Knowledge Armor: The shared expert executes for 100% of tokens. In 256 micro-expert models, IQ4_NL non-linear codebooks preserve heavy-tailed outlier representations far better than standard linear quantization. |
| Core MoE Layers (Middle: 10–29) | Flat IQ3_S / Q3_K_S (uniform bit-rate across all layers) | Aggressive IQ2_S (2.50 BPW) | IQ3_XXS (3.06 BPW) + calibrated imatrix | Above the Quality Threshold: Generic 2-bit IQ2_S baselines drop below the critical quality floor for 35B MoEs, resulting in perplexity spikes on reasoning tasks. Our IQ3_XXS with imatrix achieves deep compression (272 MiB → 98 MiB per block) without sacrificing logic. |
| Edge MoE Layers (Layers 0–9 & 30–39) | Flat IQ3_S / Q3_K_S (no layer-wise gradient) | Q3_K (limited to first/last 5 layers only: L0–4, L35–39) | IQ3_S (expanded to 10 input & 10 output layers) | Protected Ingestion & Synthesis: Half of the model's layers (10 at input, 10 at output) form a non-linear armored envelope, preventing prompt misunderstanding and token degeneration across 256 micro-experts. |
| Normalization & Biases | Often degraded | Standard | F32 uncompressed | Numerical Stability: Prevents cumulative floating-point underflow/overflow across deep 40-layer computation. |
Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)
Estimated Projections on Consumer Hardware
You do not need an expensive workstation to run a cutting-edge 35B Mixture-of-Experts coding model. Estimated throughput projections on a standard consumer laptop (Intel Core i5 / AMD Ryzen, 4GB/6GB Laptop GPU, 32GB DDR4/DDR5 RAM):
- GPU VRAM Allocation: Uses only approx. 3.8 GB VRAM (fits effortlessly on budget 4GB/6GB laptop GPUs such as RTX 3050, 4050, or 2060).
- System Memory Offload: Standard 32GB system RAM accommodates the remaining layers.
- Estimated Document / Code Ingestion (Prefill): 300 to 450+ tokens/second sustained across long prompt files.
- Estimated Streaming Generation: 20 to 24+ tokens/second sustained output across system RAM!
[!TIP] Pro Tip for Consumer Laptop Users: Because the bulk of the model runs from system memory in partial offload mode, standard autoregressive generation streams seamlessly at 20 to 24+ tokens/second across everyday DDR4/DDR5 memory buses, perfectly sufficient for real-time IDE pair programming!
The 24GB Miracle: Full 256K Context Runs In VRAM!
For developers running 24GB GPUs (RTX 3090, RTX 4090, or professional workstations), standard community 3-bit or 4-bit quants weigh 15.8 to 19.5 GiB in weights alone. When combined with KV cache and compute buffers for large codebases, they trigger immediate CUDA Out-Of-Memory crashes.
KAT-Coder APEX-I-MiniPlus-V2 fits massive contexts entirely within 24GB VRAM:
| Context Length | Model Weights (Est.) | KV Cache (q8_0, 4 slots) | Compute Buffers | Total GPU VRAM (Est.) | Hardware Feasibility |
|---|---|---|---|---|---|
| 32,768 (32k) | 13.64 GiB | 0.58 GiB | 1.80 GiB | 16.02 GiB | Full offload on 24GB; partial on 16GB |
| 65,536 (64k) | 13.64 GiB | 0.92 GiB | 1.95 GiB | 16.51 GiB | Effortless fit on 24GB GPUs |
| 131,072 (128k) | 13.64 GiB | 1.58 GiB | 2.22 GiB | 17.44 GiB | Effortless fit on 24GB GPUs |
| 262,144 (256k) | 13.64 GiB | 2.92 GiB | 2.80 GiB | 19.36 GiB | FULL 256K CODE REPO IN VRAM! |
Note: Projections estimate approx. 4.64 GiB of headroom remaining on 24GB cards for system display buffers and tooling.
Hardware Throughput Projections (RTX 30 / 40 / 50)
When running with full GPU offload (-ngl 99), KAT-Coder's fine-grained MoE architecture (approx. 3.2B active parameters) unlocks extraordinary generation throughput:
| Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | Engineering Highlights |
|---|---|---|---|---|
| NVIDIA RTX 5080 / 5090 (Blackwell) | Full GPU (-ngl 99) | approx. 247 – 251 tok/s | 2,800 – 3,900+ tok/s | Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference) |
| NVIDIA RTX 4090 (24GB GDDR6X) | Full GPU (-ngl 99) | 80 – 105+ tok/s | 1,800 – 2,600+ tok/s | Near-instantaneous code completion & refactoring |
| NVIDIA RTX 3090 (24GB GDDR6) | Full GPU (-ngl 99) | 65 – 80+ tok/s | 1,400 – 2,000+ tok/s | Full 256k repository context in dedicated VRAM |
| NVIDIA RTX 4080 / 5070 (16GB) | Partial offload (approx. 30 layers) | 35 – 45+ tok/s | 800 – 1,200+ tok/s | High-efficiency local coding assistant |
| Consumer Laptop (4GB GPU + 32GB RAM) | Hybrid Offload | 20 – 24+ tok/s | 300 – 450+ tok/s | Smooth streaming from system DDR4/DDR5 RAM |
⚙️ Recommended Generation Parameters (Kwaipilot Official)
Official sampling hyperparameters specified by Kwaipilot across benchmark evaluation tracks:
| Evaluation Track / Workload | Temperature | Top-P | Top-K | Presence Penalty | Max Tokens | Thinking Mode |
|---|---|---|---|---|---|---|
| SWE-bench & Agent Coding (Official) | 1.00 | 0.95 | 20 | 1.50 | 81,920 | enable_thinking: true |
| Terminal-Bench & Direct Code Execution | 0.70 | 1.00 | 20 | 1.50 | 32,768 | enable_thinking: false |
| PinchBench & SciCode Scientific Logic | 0.60 – 0.70 | 1.00 | 20 | 1.50 | 32,768 | preserve_thinking: true |
[!IMPORTANT]
🔍 Model Inherent Behavior vs. Quantization Fidelity Notice
Any behavioral nuances, stylistic tendencies, domain-specific 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 uncompressedF32(zero router drift), armoring the token output head inQ6_K, and safeguarding attention gates inQ8_0. Empirical verification confirms near-zero perplexity loss (ΔPPL ≈ +0.06), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.
[!TIP]
💡 Developer Tip for Autonomous Coding & CI Agents (Import Discipline)
In independent evaluations of the APEX-I-MiniPlus architecture (Occamy V2 reference), reasoning and code generation scored a remarkable 90%–93.3% resolution rate on multi-file SWE benchmarks. When deploying autonomous coding agents in production, best practices include specifying in your system prompt: "Always declare complete, explicit import statements at the beginning of the file" or pairing with an automated linter (
ruff) to guarantee clean, zero-shot execution.
7. Community Compute Fund & Student Support
All IsValorum quantizations will always remain completely free and open to the public without paywalls.
However, cloud GPU compute is expensive and funded entirely out-of-pocket. If you find these handcrafted builds to be of high quality and they benefit your day-to-day workflow, you can support future Day-0 releases on Ko-fi. Every contribution directly funds the cloud compute hours required for new model runs and helps support my university education:
(When supporting on Ko-fi, feel free to leave a note with your Hugging Face handle and the specific model you would like prioritized).