base_model: Accio-Lab/occamy-1.0 base_model_relation: quantized quantized_by: IsValorum library_name: gguf pipeline_tag: text-generation language:

• en
• zh
• es
• fr
• de
• pt
• it
• ru
• ja
• ko
• vi
• th
• ar tags:
• abliterated
• uncensored
• alignment
• gguf
• quantized
• quantization
• apex
• apex-quant
• apex-i-nanoplus
• nanoplus
• custom-quantization
• unsloth-studio
• moe
• reasoning
• llama.cpp
• qwen35moe
• multimodal
• vision license: apache-2.0

Quick Navigation Index

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 & Reasoning Effort
10. Optional Support

Occamy-1.0 APEX-I-NanoPlus-V2.1 Abliterated GGUF (Uncensored / Zero Refusal)

The Next-Generation Frontier MoE · Extreme 12–13GB 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:

• Occamy-1.0 APEX-I-NanoPlus — the standard aligned NanoPlus release for broad reasoning and multimodal workflows.
• Occamy-1.0 APEX-I-MiniPlus-V2.1 Abliterated — the companion V2.1 refusal-ablated edition (14.75 GB / 13.74 GiB) delivering Q5_K_M tier fidelity with a strict 0% 2-bit quantization policy for 24GB+ workstations.
• Occamy-1.0 APEX-I-MiniPlus-V2.1 — the standard safety-aligned edition with default guardrails.

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE approx. 12–13 GB CEILING

This APEX-I-NanoPlus-V2.1 Abliterated release delivers an ultra-compact architectural configuration for sparse Mixture-of-Experts quantization within an extreme approx. 12–13 GB envelope (12.54 GB / 11.68 GiB, 2.90 BPW). Every tensor across its 40 layers and 256 micro-experts has been mathematically allocated to maximize reasoning precision, preserve routing behavior, and eliminate AVX2 CPU dequantization stalls during hybrid and system-RAM inference.

[!TIP]

AUTHENTIC DIRECTIONAL ABLITERATION (UNCENSORED)

This is the official Abliterated / Uncensored edition of Occamy-1.0 APEX-I-NanoPlus V2.1, quantized directly from the authentic unquantized directional source with importance matrix (imatrix) calibration:

• Zero Moralizing Refusals: Complete elimination of refusal vectors across cybersecurity, penetration testing, compliance, and systems programming tasks.
• Orthogonal Activation Steering: Refusal directions are isolated orthogonally to preserve 100% of domain knowledge, mathematical deduction, and MoE routing logic.
• 16GB VRAM Optimization: Leaves over 3 GB of free VRAM on 16GB consumer graphics cards (such as RTX 4080 and RTX 4070 Ti Super) for deep 32K context windows without GPU out-of-memory errors.

[!TIP]

BUILD & VERIFIED REFERENCE COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity	Quality Tier Equivalent
Unquantized BF16 Base	approx. 70.0 GB	approx. 65.2 GiB	16.00 BPW	approx. 6.18 (Reference)	Full precision baseline
APEX-I-MiniPlus V2.1 Abliterated	14.75 GB	13.74 GiB	3.52 BPW	approx. 6.24 (Reference)	Near-lossless Q5_K_M tier (0% 2-bit policy)
APEX-I-NanoPlus-V2.1 Abliterated (CURRENT)	12.54 GB	11.68 GiB	2.90 BPW	Calibrated near baseline	Solid Q4_K_M Tier at 82.1% weight reduction

Looking for the uncompromised 0% 2-bit tier? APEX-I-MiniPlus V2.1 Abliterated offers the 14.75 GB (3.52 BPW) release, protecting 100% of all expert weights above 3 bits.

Optimization History & Transparency Notice

We maintain our releases publicly as a transparent engineering record of continuous optimization:

[!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 Abliterated MoE Collection.

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

How the handcrafted APEX-I-NanoPlus-V2.1 architecture compares against standard flat quantizations in llama.cpp on 35B Mixture-of-Experts architectures:

Model Files & Technical Specifications

• Base Model: Accio-Lab/occamy-1.0
• Total Tensors: Exactly 733 tensors across 40 layers and 256 micro-experts
• Context Length: 262,144 tokens (native 256K)

Surgical Tensor Quantization Map (Audited from GGUF)

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

Inference Quickstart

1. llama-cli (Console Generation)

llama-cli \
  -m Occamy-1.0.APEX-I-NanoPlus-V2.1-Abliterated.gguf \
  --mmproj mmproj-Q8_0.gguf \
  -p "<|im_start|>user\nHello! Explain your architecture.<|im_end|>\n<|im_start|>assistant\n" \
  -ngl 99 -c 4096 --temp 0.6 --top-p 0.95

2. llama-server (OpenAI-Compatible API)

llama-server \
  -m Occamy-1.0.APEX-I-NanoPlus-V2.1-Abliterated.gguf \
  --mmproj mmproj-Q8_0.gguf \
  --port 8080 \
  -ngl 99 -c 16384

3. Speculative Decoding with MTP Draft Head (1.8x to 2.3x Speedup)

llama-server \
  -m Occamy-1.0.APEX-I-NanoPlus-V2.1-Abliterated.gguf \
  --draft-model occamy-1.0-mtp-Q4_K_M.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  --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.

Resolution:

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 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. Native reasoning_effort Multi-Level Control:
   • low / minimal: Keeps internal thinking concise and focused strictly on immediate execution steps to minimize latency in automated loops.
   • medium (default): Balanced, structured reasoning process with standard analytical depth.
   • high / xhigh: Guides the model to formulate a clear implementation plan upfront before generating code, avoiding circular self-doubt loops.
   • none / off: Closes the thinking block immediately (<think>\n\n</think>) when reasoning is disabled.
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 Occamy-1.0.APEX-I-NanoPlus-V2.1-Abliterated.gguf --chat-template-file chat_template.jinja --reasoning-effort medium

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).