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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.

1. Optimization History & Transparency Notice
2. Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations
3. Model Files & Technical Specifications
4. Surgical Tensor Quantization Map (Audited from GGUF)
5. Inference Quickstart
6. 1. llama-cli (Console Generation)
7. 2. llama-server (OpenAI-Compatible API)
8. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
9. Hardened Agentic Chat Template
10. Optional Support

Ornith-1.5-35B-A3B APEX-I-NanoPlus GGUF

The Next-Generation Frontier MoE · Extreme 12–13 GB Footprint · Fast System RAM Streaming & Massive Context on 16GB VRAM

[!NOTE]

🚀 EXPLORE THE ESTABLISHED 35B MoE MINIPLUS & NANOPLUS LINEUP

These are complementary APEX-I releases, not alternate downloads of the same model. Each receives the same surgical tensor-by-tensor approach and a design suitable for full or partial system-RAM inference:

• Ornith-1.5-35B-A3B APEX-I-MiniPlus-V2.1 — software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents (15.23 GB / Q5_K_L tier, bordering Q6_K).
• Qwen3.6-35B-A3B-MTP APEX-I-NanoPlus — the approx. 13.0 GB NanoPlus pioneer achieving Q4 quality at 2.93 BPW.
• Occamy-1.0 APEX-I-NanoPlus — versatile frontier MoE for deep research and multimodal tasks.

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 12–13 GB CEILING (STREAMLINED ARCHITECTURE)

This APEX-I-NanoPlus release is a streamlined tensor-by-tensor configuration for sparse Mixture-of-Experts quantization within a 12–13 GB envelope. It omits an MTP companion in order to prioritize the 40-layer backbone in a compact 12.55 GB (11.69 GiB) main GGUF.

[!TIP]

🏆 BUILD & VERIFIED REFERENCE COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity	ΔPPL vs. approx. BF16	Quality Tier Equivalent
Unquantized BF16 Base	approx. 71.0 GB	approx. 66.2 GiB	16.00 BPW	approx. 7.58 (Reference)	0.000	Full precision baseline
APEX-I-MiniPlus V2.1	15.23 GB	14.18 GiB	3.43 BPW	7.6370 ± 0.21010	+0.0570 (+0.75%)	Q5_K_L tier (bordering Q6_K)
APEX-I-NanoPlus (CURRENT)	12.55 GB	11.69 GiB	approx. 2.93 BPW	8.0913 ± 0.22535	+0.5113 (+6.75%)	Solid Q4_K_M / Q4_K_L Tier

Looking for higher precision? Ornith-1.5-35B-A3B APEX-I-MiniPlus-V2.1 offers the full 15.23 GB (3.43 BPW) release of this Ornith family, delivering Q5_K_L tier (bordering Q6_K) fidelity for repository-scale software engineering.

Routing: all recipe-designated gate_inp and gate_shexp tensors remain in uncompressed F32, preserving zero routing drift.

Evaluation status: WikiText-2 perplexity successfully measured on final GGUF (8.0913 ± 0.22535).

ARC-Challenge (0-shot, 1,172 questions): approx. 95.71%.

• Q4_K_L Tier in Reasoning & Routing: 100% uncompressed F32 routers (gate_inp) and a Q6_K output head eliminate router drift, matching or exceeding standard Q4_K_L baselines on logic benchmarks.
• Solid Q4_K_M Tier in Language Modeling: WikiText-2 perplexity preserves 4-bit distributional fidelity across standard generation in an ultra-lean footprint.

[!WARNING]

DO NOT CONFUSE APEX-I-NANOPLUS WITH GENERIC COMMUNITY SUB-3-BIT QUANTS!

Regardless of release version, NEVER confuse handcrafted APEX-I-NanoPlus builds with generic community sub-3-bit releases:

• Generic Community IQ2_S / IQ2_XXS: Uniformly crushes all core MoE experts down to aggressive 2-bit codebooks without importance calibration, leaves the sensitive token output head unarmored at 3-bit, and compresses attention projections. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.
• Handcrafted APEX-I-NanoPlus: Applies a surgical tensor-by-tensor architecture that preserves 100% of expert routing matrices in uncompressed F32 (zero router drift), armors the token output head in high-precision Q6_K, safeguards attention gates in Q8_0, fortifies the critical MoE down-projection residual stream (ffn_down_exps) in IQ3_XXS (3.06 bpw), and restricts 2-bit compression strictly to redundant gating/up projections guided by the official imatrix.

[!TIP]

SYSTEM RAM INFERENCE: FULL OR PARTIAL

This APEX-I-NanoPlus 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.

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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 architectures:

[!TIP]

Deployment & System Architecture Guide

• Full GPU VRAM Offload (16GB+ VRAM, -ngl 99): Effortless full offload with native 32K–64K context support on 16GB cards (RTX 4080 / RTX 4070 Ti Super), and native 256K context on 24GB workstations (RTX 3090 / 4090 / 5090).
• System RAM Streaming Specialist (DDR4/DDR5 & Massive Context): Specially engineered to run either partially or entirely out of system RAM across large or full context windows. By utilizing linear SIMD-optimized Q4_K attention projections and preserving critical down-projections in IQ3_XXS, AVX2 CPU dequantization stalls are eliminated.

Explore our official collection: APEX-I-NanoPlus Collection.

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Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations

How the handcrafted APEX-I-NanoPlus architecture compares against standard flat quantizations in llama.cpp on 35B Mixture-of-Experts architectures:

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Model Files & Technical Specifications

• Base Model: ornith-ai/Ornith-1.5-35B-A3B
• Parameters: 35.2B total (approx. 2.6B active per token)
• Architecture: 40 layers, 256 micro-experts (8 active per token) + hybrid linear attention / DeltaNet recurrent layers + periodic full attention (layers 3, 7, 11, 15, 19, 23, 27, 31, 35, 39)
• Context Length: 262,144 tokens (native 256K)

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Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights:

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Inference Quickstart

1. llama-cli (Console Generation)

llama-cli \
  -m Ornith-1.5-35B-A3B.APEX-I-NanoPlus.gguf \
  --mmproj mmproj-Q8_0.gguf \
  -p "<|im_start|>user\nWrite a complete Rust implementation of a concurrent ring buffer.<|im_end|>\n<|im_start|>assistant\n" \
  -ngl 99 -c 8192 --temp 0.6 --top-p 0.95

2. llama-server (OpenAI-Compatible API)

llama-server \
  -m Ornith-1.5-35B-A3B.APEX-I-NanoPlus.gguf \
  --mmproj mmproj-Q8_0.gguf \
  --port 8080 \
  -ngl 99 -c 16384

[!IMPORTANT]

CRITICAL ADVISORY FOR CODING WORKFLOWS: PREVENTING SYNTAX & TOKEN SWAPPING

In programming code, brackets ({, }), assignment operators (=), and indentation whitespace repeat constantly across multi-line structures.

Common Issue: Many local frontends (such as LM Studio defaults, Ollama, or web interfaces) ship with repeat_penalty set to 1.1 or 1.15. While this prevents loops in creative prose, applying repeat penalties to code artificially penalizes necessary syntax tokens. When the logit of { drops, the model is forced to emit the next closest mathematical token (= or [), resulting in character swapping or dropped/doubled whitespace.

Verified Upstream Behavior: This self-correcting behavior (where the model notices the mistake in its thinking loop but repeats the substitution) is documented on official upstream base checkpoints and Q8 builds (Ornith Discussion #34 and Discussion #22). It is completely eliminated by proper sampling configuration:

1. Disable Repeat Penalties (Required for Code):
   • repeat_penalty: 1.0 (strictly disabled)
   • presence_penalty: 0.0
   • frequency_penalty: 0.0
2. Calibrate Samplers:
   • temperature: 0.60 (or 0.20 - 0.30 for strict, deterministic code syntax)
   • min_p: 0.05 (prunes low-probability noise tokens effectively)
   • top_p: 0.95
   • top_k: 20
3. Native Jinja Formatting: Always pass the --jinja flag so the tokenizer handles leading-space BPE tokens ( { vs {, = vs =) cleanly.

[!TIP]

HARDENED AGENTIC CHAT TEMPLATE (JINJA)

An optimized chat_template.jinja is included at the root of this repository. It hardens agent workflows and multi-turn stability:

1. Binary Thinking Control: Clean native toggle via enable_thinking: true/false preserving native model behavior.
2. Tool-Calling Safeguard (Anti-Premature Stop): Prevents the model from terminating a turn (<|im_end|>) at a colon or action declaration prior to outputting <tool_call>.
3. Multi-Turn Thinking Memory: Preserves historical <think> blocks across turns by default, preventing context distribution drift in 78K+ token runs.

Usage with llama-server:

llama-server -m Model.gguf --chat-template-file chat_template.jinja

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).