IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

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Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.

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. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
7. Hardened Agentic Chat Template & Reasoning Effort
8. 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-NanoPlus GGUF

The Next-Generation Frontier MoE · Extreme 18GB Footprint · Fast System RAM Streaming & Massive Context on 16GB–24GB VRAM

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).

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	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 (CURRENT)	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 Q3_K_S	20.41 GB	19.01 GiB	3.10 BPW	aprox. 30.75 - 31.20	+0.65 a +1.10 (+2.9%)	High syntax degradation
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

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Model Files & Technical Specifications

File Name	File Size	Memory Footprint	BPW	Description
Qwen3.8-Flash-Coder-85GB.APEX-I-NanoPlus.gguf	18.34 GB (17.08 GiB)	17.08 GiB	2.90 BPW	Ultra-compact agentic coding MoE achieving solid Q4 quality with massive context capability

• Base Model: Jab1718/qwen3.8-flash-coder-85gb-bf16
• 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, 160 MoE experts with 10 active)
• Context Length: 262,144 tokens (native 256K)

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Surgical Tensor Quantization Map (Audited from GGUF)

Layer Group	Sub-Component / Tensor	Qty	Precision	Engineering Rationale
Global Output Head	output.weight	1	Q6_K	Armored in high-precision Q6_K to preserve token classification.
Global Embeddings	token_embd.weight	1	Q3_K	Compact 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.
Attention Gates	blk.*.attn_gate.weight (36 SSM Layers)	36	Q8_0	High-precision attention gating across hybrid DeltaNet recurrence layers.
Linear Attention Projections	blk.*.attn_qkv.weight (36 SSM Layers)	36	Q3_K	Efficient 3-bit quantization for SSM attention state inputs.
SSM Linear Output	blk.*.ssm_out.weight (36 SSM Layers)	36	Q5_K	5-bit precision for linear state-space recurrence output.
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 attention checkpoints for deep retrieval.
Periodic Sparse Attention Output	blk.{3,7,...}.attn_output.weight (12 Layers)	12	Q6_K	Armored 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	Q5_0	ne0=640 in standard block-32; zero divisibility crashes.
Shared Foundation Experts	blk.*.ffn_{gate,up}_shexp.weight (All 48 Layers)	96	Q5_K	Preserves core coding knowledge active on 100% of tokens.
Highway Connections (Down/Inject)	blk.*.hc_{attn,ffn}_{down,inject}.weight	192	Q6_K	High-fidelity residual highway bypass.
Highway Connections (Up)	blk.*.hc_{attn,ffn}_up.weight	96	Q5_0	ne0=320 in standard block-32 format.
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	IQ2_S (36) / IQ2_XXS (36) / IQ3_XXS (24)	Scaled expert density: sub-2.5 BPW on deep layers, IQ3_XXS 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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Inference Quickstart

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

CRITICAL ADVISORY FOR CODING WORKFLOWS: PREVENTING SYNTAX & TOKEN SWAPPING

Disable repeat penalties (repeat_penalty: 1.0, presence_penalty: 0.0, frequency_penalty: 0.0) and use --jinja to avoid syntax bracket substitutions.

7. Community Compute Fund & Priority Model Requests

All IsValorum quantizations will always remain completely free and open to the public without paywalls.

However, cloud GPU compute is expensive. If you find these builds valuable and would like to support the project or request a specific model architecture to be prioritized for the next MiniPlus/NanoPlus release, you can sponsor GPU compute time through Ko-fi:

(When supporting on Ko-fi, feel free to leave a note with your Hugging Face handle and the specific model you would like prioritized).