Instructions to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Use Docker

docker model run hf.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

LM Studio
Jan
Ollama
How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
```
Unsloth Desktop

How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF
```

Lemonade

How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "IsValorum/Qwen3.8-Flash-Coder-APEX-I-NanoPlus-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Quick Navigation Index

Optimization History & Transparency Notice
Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations
Model Files & Technical Specifications
Surgical Tensor Quantization Map (Audited from GGUF)
Inference Quickstart
CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
Hardened Agentic Chat Template & Reasoning Effort
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

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

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)

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.

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.