pbhappliedsystems/qwen3.6-27B-gguf-F16

Qwen3.6-27B · GGUF F16

Converted by [PBH Applied Systems, LLC](https://pbhappliedsystems.com) — Applied AI/ML Consulting · LLM Optimization & Deployment · Quantized AI Infrastructure

📌 Provenance repository — no behavioral evaluation performed. This repository contains the full-precision F16 GGUF of Qwen3.6-27B. At 53.8 GB, the F16 artifact exceeds the VRAM capacity of the evaluation hardware (NVIDIA RTX 4090, 24 GB). All behavioral evaluation data for this model is in the Q4\_K\_M companion repository: `pbhappliedsystems/qwen3.6-27B-gguf-Q4-K-M`.

🆕 First Qwen3-series model in the PBH Applied Systems evaluated series. Qwen3 introduces hybrid (adaptive) thinking mode — the model generates extended chain-of-thought reasoning on harder tasks. See the Q4\K\M card for a full analysis of how this behavior interacts with structured output evaluation.

Why No Evaluation

In the PBH Applied Systems evaluation pipeline, F16 GGUFs serve as cache-generation baselines for Q4\K\M comparison runs. For this model, the F16 GGUF is 53.8 GB — loading it into the RTX 4090 (24 GB VRAM) for a valid baseline run is not possible. The Q4\K\M run (20260426_163540) was therefore run as a standalone evaluation without an F16 cache baseline.

For all behavioral results, cross-series comparisons, thinking mode analysis, and deployment guidance, see the Q4\_K\_M card.

Model Description

This repository contains the full-precision F16 GGUF of `Qwen/Qwen3.6-27B`, a 27-billion parameter model from Alibaba Cloud's Qwen3 generation featuring hybrid (adaptive) thinking mode.

Key Characteristics

• —Parameters: 27B
• —Architecture: Qwen3 · Hybrid thinking / non-thinking mode
• —Format: GGUF F16 (full precision)
• —File size: 53.8 GB
• —SHA256: 79ec580010d1a6690476a37436196e99b5c8fae7da75dfe2f6f3836663bf54cb
• —Minimum VRAM (full GPU offload): ~70 GB
• —Recommended hardware: 2× A100 40 GB · A100 80 GB · 3× A10G 24 GB
• —Context window: 32,768 tokens (check model config)
• —License: Apache 2.0

On thinking mode and F16 inference: At full F16 precision, Qwen3's adaptive thinking mode will generate substantially longer responses on harder tasks than at Q4\K\M, as the model has more capacity to explore extended reasoning chains. Expect significantly higher per-request latency for complex structured tasks compared to the Q4\K\M evaluation times documented in the companion card.

Artifact Provenance

The F16 GGUF was converted from Qwen/Qwen3.6-27B using a custom-built llama.cpp conversion pipeline developed by PBH Applied Systems, without modification to model weights.

Hardware Requirements

Usage

Installation

pip install llama-cpp-python huggingface_hub

For multi-GPU CUDA deployment:

CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --force-reinstall --no-cache-dir

Python — llama-cpp-python (multi-GPU) with Think-Block Stripping

from huggingface_hub import hf_hub_download
from llama_cpp import Llama
import re

# Note: 53.8 GB download — requires ~70 GB total VRAM for full GPU offload
model_path = hf_hub_download(
    repo_id="pbhappliedsystems/qwen3.6-27B-gguf-F16",
    filename="qwen3.6-27B-gguf-F16.gguf"
)

# Multi-GPU: adjust tensor_split to match your GPU configuration
llm = Llama(
    model_path=model_path,
    n_ctx=8192,
    n_gpu_layers=-1,
    tensor_split=[1, 1, 1],  # Example: 3× A10G 24 GB
    verbose=True,
)

def strip_thinking(raw: str) -> str:
    """Strip <think> blocks and EOS tokens from Qwen3 output."""
    clean = re.sub(r'<think>.*?</think>', '', raw, flags=re.DOTALL).strip()
    return re.sub(r'<\|im_end\|>', '', clean).strip()

# Use /no_think to suppress thinking mode for structured output tasks
response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a precise assistant."},
        {"role": "user", "content": "Return a JSON object with keys: summary, risk_level. /no_think"}
    ],
    temperature=0.15,
    max_tokens=2048,  # Allow space for thinking tokens at full precision
)
print(strip_thinking(response["choices"][0]["message"]["content"]))

CLI — llama-cli (multi-GPU)

llama-cli \
  --model qwen3.6-27B-gguf-F16.gguf \
  --chat-template qwen3 \
  --system-prompt "You are a precise assistant." \
  --prompt "Return a JSON object with keys: summary, risk_level. /no_think" \
  --n-predict 2048 \
  --ctx-size 8192 \
  --n-gpu-layers -1 \
  --tensor-split 1,1,1 \
  --temp 0.15

🔬 About quant_eval & This Evaluation Series

**quant_eval** is a proprietary behavioral evaluation harness developed by PBH Applied Systems, LLC. It measures real agent-adjacent task performance across structured output, tool dispatch, multi-turn state retention, and multi-step planning — not perplexity or leaderboard proxies. Every model published under `pbhappliedsystems` has been independently evaluated using quant_eval before being recommended for any production role.

See it in action: **Live AI Agent Demo →** The demo runs production-style agent workflows powered by open-weight models selected through the quant_eval evaluation pipeline.

Need a deployment recommendation? Not sure which quantization level is right for your hardware, latency target, or agent type? **→ pbhappliedsystems.com**

Evaluated and published by [PBH Applied Systems, LLC](https://pbhappliedsystems.com) · [patrick@pbhappliedsystems.com](mailto:patrick@pbhappliedsystems.com)

About PBH Applied Systems

**PBH Applied Systems, LLC** is an Oklahoma City–based applied machine learning and AI systems company specializing in production-grade model evaluation, quantization pipelines, agentic AI infrastructure, and scalable AI-driven application development.

Patrick Hill, M.S. — Founder · Data Scientist · AI/ML Engineer · Author of [Applied Machine Learning: Concepts, Tools, and Case Studies](https://a.co/d/05qat7Xz) (required reading, UAT CSC 373)

📞 Work With PBH Applied Systems

👉 [Book a Scoping Call](https://pbhappliedsystems.com) · 👉 [Request an Evaluation Report](https://pbhappliedsystems.com) — from $2,500

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License

This GGUF repository inherits the license of the base model: Apache 2.0 — `Qwen/Qwen3.6-27B`

GGUF conversion performed by [PBH Applied Systems, LLC](https://pbhappliedsystems.com) · No behavioral evaluation — see companion Q4\_K\_M repository for all evaluation data