---

base_model: AllSpark-Research/Iris-mini

quantized_by: IsValorum

library_name: gguf

language:

- en

- zh

- es

- fr

- de

- pt

- it

- ru

- ja

- ko

- vi

- th

- ar

tags:

- gguf

- quantized

- quantization

- apex

- apex-quant

- custom-quantization

- unsloth-studio

- moe

- reasoning

- llama.cpp

- qwen35moe

license: apache-2.0

pipeline_tag: text-generation

---

<a id="quick-navigation"></a>Quick Navigation Index

1. Optimization History & Transparency Notice
2. Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
3. Empirical Benchmarks & Fidelity Verification
4. Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations
5. Model Files & Technical Specifications
6. Surgical Tensor Quantization Map (Audited from GGUF)
7. Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)
8. The 24GB Miracle: Full 256K Context Runs In VRAM!
9. Recommended Configuration & Setup
10. Recommended Generation Parameters (AllSpark-Research Official)
11. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
12. Hardened Agentic Chat Template & Reasoning Effort
13. Optional Support

Iris-mini APEX-I-MiniPlus-V2.1 GGUF

The Definitive Frontier MoE · Efficient System RAM Offload · Full 256K Context on 24GB Workstations

> [!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 13–14 GB 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]

> ### 🏆 EMPIRICAL BENCHMARK & QUALITY 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. 5.31 (Reference) | Full precision baseline |

> | APEX-I-MiniPlus V2.1 (CURRENT) | 15.30 GB | 14.25 GiB | 3.40 BPW | 5.3735 ± 0.1214 (approx. ΔPPL +0.0635 / +1.20%) | Q5_K_M tier |

>

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

> [!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-bit Q3_K_M, and compresses attention projections down to Q3_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 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). 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.

---

<a id="toc-01"></a>

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:

| Specification | Core Experts (10–29) | Edge Experts (0–9, 30–39) | Shared Expert (shexp) | Full Attention (L3, 7, 11, ...) | Attention Gates (30 Layers) | Output Head (output.weight) | Routers (gate_inp) | Size / Overhead | Real-World Impact |

| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- |

| Generic APEX Mini | IQ2_S (2.50 bpw) | Q3_K (only 5 layers) | Q4_K / Q3_K | Q3_K | Compressed | Q3_K_M | Compressed | Baseline (approx. 12.5 GB) | Severe syntax errors, broken code indentation, high perplexity in <think>. |

| MiniPlus V1 | IQ3_XXS (3.06 bpw) | Q3_K (5 layers) | Q4_K / IQ4_NL | Q3_K | Q8_0 | Q6_K | F32 (uncompressed) | Baseline MiniPlus (approx. 13.56 GiB) | Lean & agile profile; runs flawlessly in system RAM; zero router drift; protects core logic. |

| MiniPlus V2.1 (CURRENT) | IQ3_XXS | Q3_K (20 layers) | Q5_K (All 40 layers) | Q4_K (q/k/v) + Q6_K (output) | Q8_0 | Q6_K | F32 | < 180 MB extra over V1 (approx. 13.74 GiB total) | Enhanced long-context stability and refined throughput under RAM offload; shared foundation experts armored across all 40 layers. |

> [!TIP]

> ### Architecture & Edition Guide — Choosing Between Editions

> - Full GPU VRAM Offload (24GB+ VRAM, -ngl 99): Both V1 and V2.1 run blistering fast on GPU tensor cores with virtually identical top-tier quality.

> - MiniPlus V1 (Ultra-Lean Footprint): If your machine has strict RAM/VRAM limits (approx. 13.56 GiB), V1 provides an uncompromising baseline.

> - MiniPlus V2.1 (System RAM Streaming Specialist with Massive Context): Specially engineered to run either partially or entirely out of system RAM across large or full (+160k to 256k) context windows. Upgrades all 40 shared foundation experts to Q5_K and armors full attention anchor layers. Depending on your processor architecture and memory bandwidth (dual-channel DDR4 or high-speed DDR5 6000+ MT/s), streaming generation in system RAM can approach speeds remarkably close to full VRAM execution, allowing you to allocate GPU VRAM exclusively for the massive KV cache required by extreme context windows while model execution streams smoothly across system RAM. All this for only approx. 180 MB more, an overhead completely negligible in system RAM.

>

> Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases.

>

> Need our leanest possible memory footprint? Explore the Iris-mini MiniPlus V1 Edition.

---

> [!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.

---

<a id="independent-benchmark"></a>

<a id="toc-02"></a>

🏅 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 V2.1 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-1.5 and Tiel-Coder 35B-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.

---

<a id="model-specifications"></a>

<a id="empirical-benchmarks"></a>

<a id="toc-03"></a>

Empirical Benchmarks & Fidelity Verification

The comparison table near the top consolidates the model-specific BF16 baseline, final GGUF PPL, delta, published main-file size, BPW, and fidelity tier. The routing treatment is preserved in the note directly beneath it.

<a id="quality-spectrum"></a>

<a id="toc-04"></a>

Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations

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

| Quantization Format | Bits Per Weight (BPW) | Model Footprint (Disk / VRAM) | Perplexity Delta (vs. FP16 Baseline) | Token Fidelity & Syntactic Stability Tier |

| :--- | :---: | :---: | :---: | :--- |

| FP16 / BF16 (Uncompressed) | 16.0 bpw | approx. 70 GB | 0.00 (Reference) | 100% full uncompressed reference fidelity. |

| Standard Q8_0 | 8.50 bpw | approx. 38 GB | approx. +0.01 | Virtually lossless; excessive memory overhead for consumer hardware. |

| Standard Q6_K | 6.56 bpw | approx. 30 GB | approx. +0.02 to +0.05 | Near-lossless FP16 fidelity; requires multi-GPU or 32GB+ VRAM setups. |

| 🏆 APEX-I-MiniPlus V2.1 (IsValorum) | 3.40 bpw | 14.75 GB (13.74 GiB) | approx. +0.06 (PPL: 5.3735 vs FP16 approx. 5.31) | Model-specific result: ΔPPL approx +0.06 (PPL: 5.3735 vs FP16 approx 5.31), supporting a Q5_K_M-class fidelity tier at less-than-Q3_K_M weight at less-than-Q3_K_M weight, at an 80% VRAM reduction. Full native 256K context on standard 24GB workstations. |

| Standard Q5_K_M | 5.50 bpw | approx. 25 GB | approx. +0.05 to +0.10 | Commercial transparent threshold; exceeds standard single 24GB GPU limits. |

| Standard Q4_K_M | 4.50 bpw | approx. 20 GB | approx. +0.15 to +0.25 | Standard industry trade-off; requires context offload compromises. |

| Standard Q3_K_M / Q3_K_S | 3.44 bpw | 17.2 GB | approx. +0.40 to +0.85 | Noticeable syntax drop, bracket corruption, and tokenizer classification noise. |

| Standard IQ2_S / Generic APEX Mini | 2.50 bpw | approx. 12.5 GB | approx. +1.50 to +3.00+ | Severe reasoning breakdown, high perplexity spikes in <think> chains. |

<a id="toc-05"></a>

Model Files & Technical Specifications

| File Name | File Size | Memory Footprint | BPW | Description |

| :--- | :--- | :--- | :--- | :--- |

| Iris-mini.APEX-I-MiniPlus-V2.1.gguf | 14.75 GB (13.74 GiB) | 13.74 GiB | 3.40 BPW | Linear vector reasoning, mathematical problem solving & algorithmic synthesis MoE |

• Base Model: AllSpark-Research/Iris-mini
• Parameters: 35.2B total (approx. 2.6B to 3.2B active per token)
• Architecture: 40 main layers, 256 micro-experts (8 active per token) + hybrid linear attention / DeltaNet recurrent layers + integrated blk.40 MTP block
• Context Length: 262,144 tokens (native 256K)

---

<a id="tensor-map"></a>

<a id="toc-06"></a>

Surgical Tensor Quantization Map (Audited from GGUF)

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

| Layer Group | Sub-Component / Tensor | Qty | Precision | Engineering Rationale |

| :--- | :--- | :---: | :---: | :--- |

| Global Output Head | output.weight | 1 | Q6_K | Preserves near-FP16 token classification; eliminates syntax errors, bracket drops, and hallucinations. |

| Global Embeddings | token_embd.weight | 1 | Q4_K | High-fidelity vocabulary embedding representation. |

| All Normalizations | output_norm, attn_*_norm, post_attention_norm, ssm_norm, MTP nextn norms | 138 | F32 | Uncompressed numerical stability across the 40 main layers and integrated MTP block. |

| Expert Routers | blk.*.ffn_gate_inp, ffn_gate_inp_shexp | 82 | F32 | Uncompressed routing matrices across the 40 main layers and integrated MTP block. |

| Attention Gates | blk.*.attn_gate.weight (30 Hybrid Layers) | 30 | Q8_0 | High-precision attention gating across hybrid DeltaNet recurrence layers; eliminates crosstalk. |

| Shared Foundation Experts | blk.*.ffn_{gate,down,up}_shexp (40 Main Layers + MTP) | 123 | Q5_K | Foundation knowledge backbone plus the integrated MTP shared-expert path. |

| Periodic Full Attention | blk.{3,7,11,...,39}.attn_q/k/v + MTP blk.40.attn_q/k/v | 33 | Q4_K | Full quadratic attention anchors plus integrated MTP attention projections. |

| Periodic Full Attention | blk.{3,7,11,...,39}.attn_output + blk.40.attn_output | 11 | Q6_K | Armored attention output projection over deep context, including MTP. |

| Recurrent SSM Scales | blk.*.ssm_alpha, ssm_a, ssm_conv1d, ssm_dt | 120 | F32 | Guarded in uncompressed FP32 to prevent DeltaNet recurrent state drift. |

| Linear Attention & SSM | blk.*.attn_qkv, ssm_beta, ssm_out | 90 | Q3_K | Linear AVX2 execution; zero SIMD CPU stalls during system RAM streaming. |

| Edge MoE Experts | Layers 0–9 & 30–39 (ffn_*_exps) | 60 | Q3_K | Linear SIMD execution optimized for system RAM offload. |

| Core MoE Experts | Layers 10–29 (ffn_*_exps) | 60 | IQ3_XXS | Calibrated with importance matrix (imatrix) for maximum compactness in deep layers. |

| MTP Expert Down-Proj | blk.40.ffn_down_exps.weight | 1 | Q6_K | High-precision down projection in the integrated Multi-Token Prediction block. |

| MTP Expert Gate/Up | blk.40.ffn_{gate,up}_exps.weight | 2 | Q4_K | Gate and up expert tensors in the integrated Multi-Token Prediction block. |

| MTP Projection | blk.40.nextn.eh_proj.weight | 1 | Q4_K | Dedicated hidden-state projection for the integrated Multi-Token Prediction block. |

---

<a id="throughput-projections"></a>

<a id="toc-07"></a>

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

Empirically verified in Unsloth Studio & llama.cpp:

| Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | 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) | 90 – 115+ tok/s | 2,000 – 2,800+ tok/s | Linear attention layers slash prefill latency |

| NVIDIA RTX 3090 (24GB GDDR6) | Full GPU (-ngl 99) | 72 – 88+ tok/s | 1,500 – 2,200+ tok/s | Full 256k native window in VRAM |

| Workstation / Laptop (DDR4 / DDR5 RAM) | Hybrid Offload (Few layers in VRAM) | Hardware-dependent | Hardware-dependent | Zero AVX2 CPU stalls; efficient streaming from system RAM |

• Aggressive Hybrid Offload Profile: Hybrid offload supports reasoning-enabled generation with limited VRAM while the remaining model weights stream from system RAM.

> [!NOTE]

> ### Empirical Testbed Architecture & Desktop/Server Scaling

> - Empirical Benchmark Hardware: The hybrid offload and system RAM streaming behavior documented above was measured on a consumer laptop powered by an Intel 12th Gen Alder Lake architecture featuring a hybrid design of Performance Cores (P-Cores) and Efficient Cores (E-Cores) paired with dual-channel system RAM and constrained laptop power/thermal envelopes.

> - Thread Scheduling & E-Core Contention: In hybrid architectures like Alder Lake, OS thread scheduling across background E-Cores and lower single-core mobile power limits introduce memory bandwidth and thread synchronization overhead during CPU dequantization.

> - Dramatic Scaling on Higher-End Processors: When running on desktop or server processors (such as modern AMD Ryzen 7000 / 9000 Zen 4/5 series or high-TDP Intel desktop platforms with dedicated performance cores, large L3 caches, and high-bandwidth dual- or quad-channel DDR5 running at 6000+ MT/s), streaming generation speeds and prefill throughput will scale dramatically higher, substantially exceeding these measured mobile numbers.

---

<a id="context-scaling"></a>

<a id="toc-08"></a>

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

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

| Context Length | Model Weights (Est.) | KV Cache (q8_0, 4 slots) | Compute Buffers | Total GPU VRAM (Est.) | Feasibility |

| :--- | :--- | :--- | :--- | :--- | :--- |

| 32,768 (32k) | 13.74 GiB | 0.58 GiB | 1.80 GiB | 16.12 GiB | Full offload on 24GB; partial on 16GB |

| 65,536 (64k) | 13.74 GiB | 0.92 GiB | 1.95 GiB | 16.61 GiB | Effortless fit on 24GB GPUs |

| 131,072 (128k)| 13.74 GiB | 1.58 GiB | 2.22 GiB | 17.54 GiB | Effortless fit on 24GB GPUs |

| 262,144 (256k)| 13.74 GiB | 2.92 GiB | 2.80 GiB | 19.46 GiB | FULL 256K NATIVE IN VRAM! |

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

> [!TIP]

> ### 💡 Empirical 16GB GPU Verification (Single Stream / Desktop)

> While theoretical multi-slot server buffers estimate approx. 16.5 GiB, independent hardware testing by zephel01 on an RTX 5090 (Occamy V2 Reference) confirmed that single-stream desktop inference consumes only 14.6 GB at 32,768 ctx and only 15.1 GB at 65,536 ctx (-ctk q8_0 -ctv q8_0 -fa on). This empirically proves that full 65K context runs completely in VRAM on 16GB cards (RTX 4080 / RTX 5080) without system RAM offload!

---

<a id="recommended-setup"></a>

<a id="toc-09"></a>

Recommended Configuration & Setup

llama-server.exe \
 -m Iris-mini.APEX-I-MiniPlus-V2.1.gguf \
 --port 8080 \
 --parallel 4 \
 --flash-attn on \
 --fit on \
 -c 104960 \
 --cache-type-k q8_0 \
 --cache-type-v q8_0

<a id="generation-parameters"></a>

<a id="toc-10"></a>

⚙️ Recommended Generation Parameters (AllSpark-Research Official)

Official generation guidelines specified by AllSpark-Research for deep research and search agent loops:

| Workload / Agent Mode | Temperature | Top-P | Context Strategy | Output Format |

| :--- | :---: | :---: | :---: | :--- |

| Deep Search & Tool Invocation | 0.20 – 0.60 | 0.95 | discard-all at 131K | OpenAI function calling interface |

| Exploratory Query Generation | 0.70 | 0.95 | Summary retry | Final answers wrapped in \boxed{} |

> [!IMPORTANT]

> <a id="quantization-fidelity"></a>

> ### 🔍 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 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 ≈ +0.06), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.

<a id="toc-coding-advisory"></a>

<a id="coding-advisory"></a>

> [!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.

<a id="toc-chat-template"></a>

<a id="chat-template"></a>

> [!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:

> ```bash

> llama-server -m Model.gguf --chat-template-file chat_template.jinja --reasoning-effort medium

> ```

<a id="toc-11"></a>

Optional Support

<a href="https://ko-fi.com/isvalorum"><img src="https://huggingface.co/spaces/IsValorum/MiniPlus-NanoPlus-Requests/resolve/main/assets/dance-gold-ship.gif" alt="Gold Ship dancing" width="128" align="right"></a>

If these MiniPlus or NanoPlus releases have been useful to you and you would like to support the work, you can do so voluntarily through https://ko-fi.com/isvalorum. Your contribution helps with evaluation, hosting, and future handcrafted quantizations. Every release will always remain free to download and use; there are no paywalled files, updates, or features.