Instructions to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Ollama
How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0
```

Lemonade

How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0

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/Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3-GGUF:Q8_0" \
  --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"

Qwen3.8-27B Base VAL-APEX-I MiniPlus V3 GGUF

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.

Original Qwen checkpoint · Revised V3 tensor allocation · Native 256K context

Official VAL-APEX-I MiniPlus V3 quantization of Qwen/Qwen3.8-27B. This edition quantizes the original Qwen checkpoint without applying Huihui abliteration or EfficientThink fine-tuning.

Integrated Multi-Token Prediction (MTP): This GGUF includes one MTP block (blk.64.*, 15 tensors) in the same file as the main language model. Its eight matrices use Q4_0 ×6, Q4_1 ×1 and Q6_K ×1; its seven normalization tensors remain F32. MTP speculative decoding depends on support and configuration in the inference backend.

VAL-APEX-I stands for: Vector-calibrated Asymmetric Layer-wise Outlier-preserving Recurrent-aware Unified Matrix-quantization

EXPLORE THE QWEN3.8 27B VAL-APEX-I EDITIONS

Choose a model variant and quantization profile:

Qwen3.8 27B — NanoPlus V3 — 12.18 GB.

Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3 — 14.16 GB.

Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3 — 12.18 GB. Collections: MiniPlus Abliterated 27B · MiniPlus Standard 27B · NanoPlus Abliterated 27B · NanoPlus Standard 27B

HIGH-FIDELITY 14.16GB BUILD

This V3 release uses a revised per-tensor allocation with an actual 14.16 GB file size and 4.15 whole-file BPW. Its reported WikiText-2 PPL is 6.1361 ± 0.4885, or +0.3419 (+5.90%) relative to the measured unmodified BF16 Base reference. The audit below records every tensor family, the block-0 exceptions and the integrated MTP head.

Quick Navigation Index

Quantization Metrics & Tensor Map
Independent Community Benchmarks (Shortbench / NanoBench Multi-Harness)
Model Files & Technical Specifications
Native Context & Runtime Memory
Recommended Configuration & Setup
Recommended Generation Parameters
Native Chat Template & Reasoning
Community Compute Fund & Student Support

1. Quantization Metrics

Size & Quality Metrics

Edition / quantization	File size	Whole-file BPW	WikiText-2 PPL	Δ PPL vs BF16 Base (5.7942)
BF16 Base baseline (unmodified)	54.65 GB (50.90 GiB)	16-bit	5.7942 ± 0.4531	0.0000 (0.00%)
Qwen3.8 27B Base — MiniPlus V3 (this release)	14.16 GB (13.19 GiB)	4.15	6.1361 ± 0.4885	+0.3419 (+5.90%)

Independent Hardware & Compute Transparency: Generating multi-million token calibration matrices and running certified perplexity evaluations on local architectures requires dedicated high-end cloud nodes funded 100% out-of-pocket. If you rely on these MiniPlus/NanoPlus builds for your local workloads, contributions to the IsValorum Compute Fund on Ko-fi directly sponsor the GPU hours required for future Day-0 handcrafted quantizations.

🏅 Independent Community Benchmarks (Shortbench / NanoBench Multi-Harness)

Verified Autonomous Agent Evaluation: Evaluated across the Shortbench / NanoBench autonomous coding and agentic suite (12 objective software engineering, pipeline, and system integrity tasks).

Independent multi-harness empirical evaluation conducted by community member @userr99 on a dedicated local workstation (24 GB VRAM / 64 GB RAM using llama.cpp). Evaluated across the 12-task autonomous coding and agentic suite on NanoBench (public discussion thread & test logs):

Test Harness	Total Score	Solved Tasks	Agentic (Tool Use)	Coding (Implementation)	Systems (Integrity)	Avg. Runtime	Public Evidence
Oh My Pi (OMP 18.7)	96.7%	10 / 12	100 / 100	100 / 100	86.7 / 100	aprox. 1h 47m	Discussion #2 Logs
Hermes Agent (v0.21.5)	95.4%	9 / 12	100 / 100	97.0 / 100	86.7 / 100	aprox. 1h 47m	Discussion #2 Logs
DeepSeek Harness (0.2.0-rc.2)	90.4%	9 / 12	100 / 100	97.0 / 100	66.7 / 100	aprox. 1h 47m	Discussion #2 Logs

Key Architectural Insights:

Zero Tool-Calling Degradation: Achieved a unanimous 100/100 in Agentic tasks across all three harnesses, confirming that protecting output.weight in Q6_K and DeltaNet recurrent memory states (ssm_a in F32, ssm_beta in Q8_0) prevents JSON schema rejections and state drift over multi-step workflows.
Universal Harness Compatibility: Surpassed the 90+ score barrier across all three test harnesses without entering infinite retry loops or syntax divergence.

Tensor Precision Map

Component	MiniPlus V3	NanoPlus V3	GSQ-RCO IQ3_S
Output head	`Q6_K` ×1	`Q6_K` ×1	`Q4_K` ×1
Token embeddings	`Q4_K` ×1	`Q3_K` ×1	`IQ2_S` ×1
Normalizations	`F32` ×161	`F32` ×161	`F32` ×161
SSM A (`ssm_a`)	`F32` ×48	`F32` ×48	`F32` ×48
SSM convolution (`ssm_conv1d`)	`F32` ×48	`F32` ×48	`F32` ×48
SSM time-step bias (`ssm_dt`)	`F32` ×48	`F32` ×48	`F32` ×48
SSM norm (`ssm_norm`)	`F32` ×48	`F32` ×48	`F32` ×48
Attention gates (`attn_gate`)	`Q4_0` ×1 `Q4_K` ×47	`Q4_0` ×1 `Q4_K` ×47	`IQ2_S` ×2 `IQ3_S` ×18 `IQ3_XXS` ×9 `IQ4_XS` ×12 `Q2_K` ×4 `Q4_K` ×3
Linear QKV (`attn_qkv`)	`Q4_0` ×1 `Q4_K` ×47	`Q3_K` ×47 `Q4_0` ×1	`IQ2_XS` ×1 `IQ2_XXS` ×1 `IQ3_S` ×22 `IQ3_XXS` ×13 `IQ4_XS` ×9 `Q2_K` ×1 `Q4_K` ×1
SSM alpha (`ssm_alpha`)	`F32` ×47 `Q4_0` ×1	`F32` ×47 `Q4_0` ×1	`BF16` ×48
SSM beta (`ssm_beta`)	`Q4_0` ×1 `Q8_0` ×47	`Q4_0` ×1 `Q8_0` ×47	`BF16` ×48
SSM output (`ssm_out`)	`Q4_0` ×1 `Q5_K` ×47	`Q4_0` ×1 `Q4_K` ×47	`IQ3_S` ×22 `IQ3_XXS` ×4 `IQ4_XS` ×16 `Q4_K` ×6
Full attention Q (`attn_q`)	`Q4_K` ×16	`Q3_K` ×16	`IQ2_XXS` ×1 `IQ3_S` ×3 `IQ3_XXS` ×3 `IQ4_XS` ×2 `Q2_K` ×6 `Q4_K` ×1
Full attention K (`attn_k`)	`Q5_K` ×16	`Q4_K` ×16	`IQ2_S` ×1 `IQ3_S` ×1 `IQ3_XXS` ×1 `IQ4_XS` ×8 `Q4_K` ×5
Full attention V (`attn_v`)	`Q6_K` ×16	`Q5_K` ×16	`IQ3_S` ×6 `IQ3_XXS` ×1 `IQ4_XS` ×1 `Q4_K` ×8
Full attention output (`attn_output`)	`Q6_K` ×16	`Q6_K` ×16	`IQ3_S` ×10 `IQ3_XXS` ×1 `IQ4_XS` ×2 `Q4_K` ×3
MLP down (`ffn_down`)	`IQ4_XS` ×64	`IQ3_XXS` ×48 `IQ4_XS` ×16	`IQ2_S` ×4 `IQ2_XS` ×3 `IQ3_S` ×22 `IQ3_XXS` ×7 `IQ4_XS` ×21 `Q2_K` ×1 `Q4_K` ×6
MLP gate (`ffn_gate`)	`IQ3_S` ×32 `IQ3_XXS` ×32	`IQ2_XXS` ×48 `IQ3_XXS` ×16	`IQ1_M` ×1 `IQ2_S` ×4 `IQ2_XS` ×4 `IQ2_XXS` ×1 `IQ3_S` ×15 `IQ3_XXS` ×21 `IQ4_XS` ×15 `Q2_K` ×1 `Q4_K` ×2
MLP up (`ffn_up`)	`IQ3_S` ×32 `IQ3_XXS` ×32	`IQ2_XXS` ×48 `IQ3_XXS` ×16	`IQ2_S` ×5 `IQ2_XS` ×1 `IQ2_XXS` ×2 `IQ3_S` ×25 `IQ3_XXS` ×18 `IQ4_XS` ×10 `Q4_K` ×3
Additional MTP head (`blk.64.*`)	`F32` ×7 `Q4_0` ×6 `Q4_1` ×1 `Q6_K` ×1	`F32` ×7 `Q4_0` ×6 `Q4_1` ×1 `Q6_K` ×1	—

× is the number of tensors in each format. Counts were read from the released GGUF headers; the GSQ-RCO column uses its published tensor allocation.

Both V3 files contain 866 tensors: 851 main-model tensors plus 15 tensors in the additional MTP head. The 64 main layers are 48 Gated DeltaNet + 16 full-attention; blk.64.* is the MTP head, not a seventeenth main full-attention layer.

Fallback exceptions: blk.0.attn_gate.weight, blk.0.attn_qkv.weight, blk.0.ssm_alpha.weight, blk.0.ssm_beta.weight and blk.0.ssm_out.weight are Q4_0 in both editions. In the MTP head, Q/K/V, MLP gate/up and nextn.eh_proj are Q4_0; MLP down is Q4_1, attention output is Q6_K, and the remaining seven tensors are F32.

V3 layer allocation: MiniPlus uses IQ3_S for MLP gate/up in layers 0–15 and 48–63, and IQ3_XXS in layers 16–47; every main MLP down tensor is IQ4_XS. NanoPlus uses IQ3_XXS for gate/up in layers 0–7 and 56–63, and IQ2_XXS in layers 8–55; MLP down is IQ4_XS in the outer 16 layers and IQ3_XXS in the other 48.

Complete tensor-by-tensor audit: names, formats, shapes and parameter counts.

2. Model Files & Technical Specifications

File	File size	Whole-file BPW	Contents
`Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3.gguf`	14.16 GB (13.19 GiB)	4.15 BPW	Main language model and integrated MTP head

Source: Qwen/Qwen3.8-27B.
Architecture: qwen35; 64 main layers (48 Gated DeltaNet + 16 full-attention) plus one MTP block.
Stored parameters: 27,320,697,856, including MTP tensors.
Tensor count: 866, including the 15-tensor MTP head.
Native context: 262,144 tokens.
Calibration: 1,000 chunks × 4,096 tokens (4,096,000 tokens), as reported by the author. The GGUF records 1,000 calibration chunks and 496 importance-matrix entries.
Scope: This repository currently contains the language GGUF. No companion vision projector is included.

Whole-file BPW is 8 × file bytes / stored parameter count, including the file's metadata and MTP head. It is not a uniform tensor quantization type.

3. Native Context & Runtime Memory

The model's native context is 262,144 tokens. File size describes the stored weights; runtime memory also includes the KV cache, recurrent state, compute buffers, batch settings and any optional components. Choose context and offload settings according to the allocations reported by your runtime.

4. Recommended Configuration & Setup

llama.cpp Server

llama-server \
  -m Qwen3.8-27B-VAL-APEX-I-MiniPlus-V3.gguf \
  --jinja \
  -ngl 99 \
  -c 8192 \
  --host 127.0.0.1 \
  --port 8080

The command requests GPU offload; adjust offload and context to the available memory. In LM Studio, load the GGUF and retain its embedded Qwen chat template. The integrated MTP tensors are listed in the audit; speculative decoding requires explicit support and configuration in the chosen backend.

5. Recommended Generation Parameters

These are the upstream Qwen defaults, distinct from the WikiText-2 evaluation settings.

Setting	Thinking	Non-thinking
Temperature	1.0	0.7
Top-P	0.95	0.80
Top-K	20	20
Min-P	0.0	0.0
Presence penalty	0.0	1.5
Repetition penalty	1.0	1.0

6. Native Chat Template & Reasoning

The native Qwen Jinja chat template is embedded in this GGUF. Use --jinja where supported and preserve the native formatting for messages, tool calls and thinking blocks. Reasoning controls depend on the backend's implementation of the template arguments.

8. 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).