Qwen3.8-27B-VAL-APEX-I-NanoPlus-V3-GGUF

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

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

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

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

[!NOTE]

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

Choose a model variant and quantization profile:

• Qwen3.8 27B — MiniPlus V3 — 14.16 GB (+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B EfficientThink Uncensored — MiniPlus V2.1 — 15.08 GB (+15.01% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B EfficientThink Uncensored — NanoPlus — 11.65 GB (+10.87% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B Huihui Abliterated — MiniPlus V2.1 — 15.33 GB (+13.06% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B Huihui Abliterated — NanoPlus — 11.90 GB (+9.14% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).

VAL-APEX-I collection · MiniPlus collection · NanoPlus collection

[!IMPORTANT]

COMPACT 11.35GB BUILD

This V3 release uses a revised per-tensor allocation with an actual 11.35 GB file size and 3.32 whole-file BPW. Its reported WikiText-2 PPL is 6.2577 ± 0.5011, or +0.4635 (+8.00%) 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

1. Quantization Comparison: Metrics & Tensor Map
2. Model Files & Technical Specifications
3. Native Context & Runtime Memory
4. Recommended Configuration & Setup
5. Recommended Generation Parameters
6. Native Chat Template & Reasoning
7. Optional Support

1. Quantization Comparison

Size & Quality Metrics

The NanoPlus V3 file is 11.35 GB, approximately 0.45 GB smaller than GSQ-RCO IQ3_S, with 11.49% lower reported WikiText-2 PPL (6.2577 vs 7.07). MiniPlus V3 reaches 13.21% lower reported PPL (6.1361 vs 7.07) at 14.16 GB, approximately 2.36 GB larger.

Tensor Precision Map

× 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

• 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-NanoPlus-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.

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.

7. Optional Support

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.