IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF

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

EXPERIMENTAL PRE-RELEASE NOTICE: ENGLISH-ONLY CODING SPECIALIST

This model suite is quantized from Jab1718/qwen3.8-flash-coder-85gb-bf16, which is an intermediate experimental slice created using moe-slice (352 out of 512 routed experts were permanently pruned exclusively against English Python and SWE-bench calibration datasets).

• English Coding Only: This model is strictly designed for programming, code completion, refactoring, and agentic tool-calling in English.
• Severe Multilingual & General Degradation: Because conversational and multilingual experts were pruned and the upstream author has not yet released the recovery fine-tuning pass, this model severely degrades and outputs broken text in languages other than English (e.g., Spanish, French, German, etc.) or in general chit-chat.
• Incompatible with Strata Engine: This model uses a 160-expert layout with decoupled n-gram tables; it is not compatible with Strata Engine (which requires the 512-expert monolith and 51B PLE tables). Run using stock llama.cpp (llama-server) or LM Studio.

Qwen3.8-Flash-Coder APEX-I-MiniPlus-V2.1 GGUF

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

EXPLORE THE COMPLETE QWEN3.8 FLASH CODER LINEUP

These are complementary APEX-I releases, not alternate downloads of the same model:

• Qwen3.8-Flash-Coder APEX-I-MiniPlus-V2.1 - specialist agentic coding MoE tuned for Q5-Q6 quality (21.77 GB / 3.45 BPW).
• Qwen3.8-Flash-Coder APEX-I-NanoPlus - ultra-compact footprint achieving solid Q4 quality (18.34 GB / 2.90 BPW).
• Qwen3.8-Flash-Coder-85GB Lossless BF16 GGUF - uncompressed reference baseline (85.30 GB / 16.00 BPW).

THE DEFINITIVE SPECIFICATION IN THE 21–22 GB CEILING

This APEX-I-MiniPlus-V2.1 release represents the specialized tensor-by-tensor configuration for sparse Mixture-of-Experts quantization within a 21–22 GB envelope. Every tensor across its 48 hybrid layers (36 linear SSM DeltaNet + 12 sparse full attention) and 160 MoE experts has been mathematically allocated to maximize reasoning precision, preserve routing behavior, and prevent avoidable CPU dequantization stalls.

EMPIRICAL BENCHMARK & QUALITY COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity	Delta PPL vs BF16 (%)	Quality Tier Equivalent
Uncompressed BF16 Reference	85.30 GB (79.44 GiB)	79.44 GiB	16.00 BPW	30.0975 +/- 0.1200	Baseline (0.00%)	Lossless Reference Baseline
APEX-I-MiniPlus V2.1 (CURRENT)	21.77 GB (20.27 GiB)	20.27 GiB	3.45 BPW	30.1495 +/- 1.0089	+0.0520 (+0.17%)	Q5_K_L / Q6_K Tier Boundary
APEX-I-NanoPlus	18.34 GB (17.08 GiB)	17.08 GiB	2.90 BPW	34.4199 +/- 1.1591	+4.3224 (+14.36%)	Solid Q4_K_M Tier
Standard Flat Q4_K_M	28.39 GB	26.44 GiB	4.50 BPW	aprox. 30.28 - 30.38	+0.18 a +0.28 (+0.7%)	Standard industry trade-off
Standard Flat Q3_K_M	22.22 GB	20.69 GiB	3.44 BPW	aprox. 30.55 - 30.95	+0.45 a +0.85 (+2.1%)	Noticeable syntax drop & bracket noise
Generic APEX Mini (IQ2_S)	17.73 GB	16.51 GiB	2.50 BPW	aprox. 31.60 - 33.10+	+1.50 a +3.00+ (+7.5%)	Severe reasoning breakdown

Routing Fidelity: All router gates (ffn_gate_inp, ffn_gate_inp_shexp) remain in uncompressed F32, preserving zero routing drift across 160 experts.

• Q6_K-Bordering Tier in Language Fidelity: WikiText-2 perplexity delta is exceptionally low (+0.0520 / +0.17% vs. BF16 baseline), placing overall code representation at the boundary of a 38 GB Q6_K build within an agile 21.77 GB footprint.
• High-Precision Foundation Knowledge: Shared foundation experts run in uncompressed Q8_0 (down, block-32) + Q6_K (gate/up) across all 48 layers, keeping coding syntax intact across 100% of tokens.
• Bypassed PLE Overhead: The 51B parameter N-gram table was cleanly removed (ple_layer_ids: []), enabling 100% GPU VRAM execution with zero host RAM overhead.

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

• Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit IQ2_S, 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 V2.1: Preserves specified router gates in uncompressed F32, armors the token output head in high-precision Q6_K, safeguards attention gates in Q8_0, and keeps core reasoning experts at calibrated 3-bit to 4-bit non-linear quantization.

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Optimization History & Transparency Notice

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

Specification	Core Experts (10 Active/160)	Shared Expert (shexp)	Sparse Attention (12 Layers)	Highway Connections (4-Way)	Attention Gates (36 Layers)	Output Head (output.weight)	Routers (gate_inp)	Real-World Impact
Generic APEX Mini	IQ2_S (2.50 bpw)	Q4_K / Q3_K	Q3_K	Compressed	Compressed	Q3_K_M	Compressed	High perplexity spikes, syntax errors in code generation.
MiniPlus V2.1 (CURRENT)	IQ3_XXS & IQ4_NL	Q8_0 + Q6_K (All 48 layers)	Q4_K (q/k/v) + Q6_K (output)	Q8_0	Q8_0	Q6_K	F32	Definitive Build (21.77 GB). Zero AVX2 CPU stalls, zero divisibility crashes, and full 256K context support.

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Model Files & Technical Specifications

File Name	File Size	Memory Footprint	BPW	Description
Qwen3.8-Flash-Coder-85GB.APEX-I-MiniPlus-V2.1.gguf	21.77 GB (20.27 GiB)	20.27 GiB	3.45 BPW	Frontier agentic coding, SWE-bench vulnerability analysis, reasoning & hybrid SSM/sparse MoE

• Base Model: Jab1718/qwen3.8-flash-coder-85gb-bf16 (derived from Qwen/Qwen3.8-Flash-Next sliced with moe-slice and DoRA calibration)
• Parameters: 45.8B total (approx. 3.7B active per token: 10 routed experts + 1 shared expert + dense backbone; 4.9B with vocabulary embeddings)
• Architecture: Qwen4ExpForCausalLM (48 hybrid layers: 36 linear SSM DeltaNet + 12 sparse full attention, 160 MoE experts per layer with 10 active)
• Context Length: 262,144 tokens (native 256K)
• PLE / N-Gram Table: Decoupled and bypassed (ple_layer_ids: []) for 100% GPU VRAM execution.

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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:

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 across 248k vocabulary.
All Normalizations	output_norm, attn_norm, ffn_norm, hc_norm, ssm_norm	146	F32	100% uncompressed numerical stability across all 48 layers.
Expert Routers	blk.*.ffn_gate_inp, ffn_gate_inp_shexp	96	F32	100% uncompressed routing fidelity across 160 experts; zero router drift.
Attention Gates	blk.*.attn_gate.weight (36 SSM Layers)	36	Q8_0	High-precision attention gating across hybrid DeltaNet recurrence layers; eliminates crosstalk.
Linear Attention Projections	blk.*.attn_qkv.weight (36 SSM Layers)	36	Q4_K	Balanced precision for input state-space projections.
SSM Linear Output	blk.*.ssm_out.weight (36 SSM Layers)	36	Q6_K	Armored state-space output projection; preserves recurrence channel mixing.
Recurrent SSM Parameters	blk.*.ssm_{a,alpha,beta,conv1d,dt,norm}	216	F32	Guarded in uncompressed FP32 to prevent DeltaNet recurrent state drift.
Periodic Sparse Attention	blk.{3,7,...}.attn_{q,k,v}.weight (12 Layers)	36	Q4_K	Quadratic full-attention anchor checkpoints for deep needle-in-a-haystack retrieval.
Periodic Sparse Attention Output	blk.{3,7,...}.attn_output.weight (12 Layers)	12	Q6_K	High-precision attention output projection over deep context.
QSA Sparse Attention Indexer	blk.{3,7,...}.indexer.{q,k}_proj.weight	24	BF16	High-fidelity sparse indexer projections for query-stream attention routing.
QSA Indexer Norms	blk.{3,7,...}.indexer.{q,k}_norm.weight	24	F32	Uncompressed indexer layer normalizations.
Shared Foundation Experts	blk.*.ffn_down_shexp.weight (All 48 Layers)	48	Q8_0	ne0=640 in standard block-32; eliminates divisibility validation aborts while preserving foundation knowledge.
Shared Foundation Experts	blk.*.ffn_{gate,up}_shexp.weight (All 48 Layers)	96	Q6_K	High-precision shared backbone active on 100% of tokens.
Highway Connections (4-Way)	blk.*.hc_{attn,ffn}_{down,inject,up}.weight	288	Q8_0	High-speed residual highway bypass; ne0=320 up-projections mapped to block-32 Q8_0.
Routed MoE Down-Projections	blk.*.ffn_down_exps.weight (All 48 Layers)	48	IQ4_NL (36) / Q4_0 (12)	Non-linear codebook quantization for SSM layers, Q4_0 for anchor layers.
Routed MoE Gate/Up Projections	blk.*.ffn_{gate,up}_exps.weight (All 48 Layers)	96	IQ3_XXS (72) / Q4_K (24)	Calibrated with imatrix for maximum compactness, with Q4_K protection on anchor layers.
Residual Output Highway	output_hc_{down,up}.weight	2	Q4_0	Low-rank residual highway projections at model termination.
Residual Output Highway Norm	output_hc_norm.weight	1	F32	Final residual normalization anchor.

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

Hardware Target	Offload Mode	Generation Speed (Est.)	Prompt Prefill Speed (Est.)	Highlights
NVIDIA RTX 5080 / 5090 (Blackwell)	Full GPU (-ngl 99)	approx. 210 – 240 tok/s	2,600 – 3,500+ tok/s	Blistering throughput on Hopper / Blackwell architectures
NVIDIA RTX 4090 (24GB GDDR6X)	Full GPU (-ngl 99)	80 – 105+ tok/s	1,800 – 2,400+ tok/s	Hybrid linear attention slashes prefill latency
NVIDIA RTX 3090 (24GB GDDR6)	Full GPU (-ngl 99)	65 – 82+ tok/s	1,300 – 1,800+ 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

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

Qwen3.8-Flash-Coder-85GB APEX-I-MiniPlus-V2.1 fits deep context windows within standard 24GB and 32GB GPUs:

Context Length	Model Weights	KV Cache (q8_0)	Compute Buffers	Total GPU VRAM (Est.)	Feasibility
32,768 (32k)	20.27 GiB	0.45 GiB	1.50 GiB	22.22 GiB	Effortless fit on 24GB GPUs (RTX 3090 / 4090)
65,536 (64k)	20.27 GiB	0.85 GiB	1.65 GiB	22.77 GiB	Full offload on 24GB GPUs
131,072 (128k)	20.27 GiB	1.45 GiB	1.90 GiB	23.62 GiB	Full offload on 24GB GPUs
262,144 (256k)	20.27 GiB	2.60 GiB	2.40 GiB	25.27 GiB	Full offload on 32GB (RTX 5090) or partial RAM offload

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Recommended Configuration & Setup

llama-server \
  -m Qwen3.8-Flash-Coder-85GB.APEX-I-MiniPlus-V2.1.gguf \
  --jinja \
  -ngl 99 \
  --ctx-size 65536 \
  --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 \
  --port 8080

Recommended Generation Parameters

Hyperparameter	Value	Description / Creator Notice
Temperature	0.60	Recommended default for code generation & reasoning (--temp 0.6).
Top-P	0.95	Source sampling parameter (--top-p 0.95).
Top-K	20	Source sampling parameter (--top-k 20).
Min-P	0.00	Source sampling parameter (--min-p 0.00).
Template Engine	--jinja	Recommended official chat template flag.
Context Size	65536	High-throughput 64K context (up to native 256K).

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.

Eliminating Character Swapping:

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.

HARDENED AGENTIC CHAT TEMPLATE (JINJA)

The repository supports full Jinja chat templating with multi-level reasoning effort 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.

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).