Instructions to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pbhappliedsystems/qwen3.6-27B-gguf-F16",
	filename="qwen3.6-27B-gguf-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
# Run inference directly in the terminal:
llama-cli -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
# Run inference directly in the terminal:
llama-cli -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

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 pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
# Run inference directly in the terminal:
./llama-cli -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

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 pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

Use Docker

docker model run hf.co/pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

LM Studio
Jan
Ollama
How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Ollama:
```
ollama run hf.co/pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
```

Unsloth Studio

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pbhappliedsystems/qwen3.6-27B-gguf-F16 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pbhappliedsystems/qwen3.6-27B-gguf-F16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pbhappliedsystems/qwen3.6-27B-gguf-F16 to start chatting

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pbhappliedsystems/qwen3.6-27B-gguf-F16:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

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

Run Hermes

hermes

Docker Model Runner
How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Docker Model Runner:
```
docker model run hf.co/pbhappliedsystems/qwen3.6-27B-gguf-F16:F16
```

Lemonade

How to use pbhappliedsystems/qwen3.6-27B-gguf-F16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pbhappliedsystems/qwen3.6-27B-gguf-F16:F16

Run and chat with the model

lemonade run user.qwen3.6-27B-gguf-F16-F16

List all available models

lemonade list

Qwen3.6-27B · GGUF F16

Converted by PBH Applied Systems, LLC — 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

Artifact	Format	Size	SHA256	Evaluated
`qwen3.6-27B-gguf-F16.gguf`	GGUF F16	53.8 GB	`79ec580010d1a6690476a37436196e99b5c8fae7da75dfe2f6f3836663bf54cb`	❌ VRAM constraint
Q4_K_M (companion repo)	GGUF Q4_K_M	16.5 GB	`c863357b1b532a02c47ca363ab666dd623470a152a291dac6619ed7ce751d8c8`	✅ Run `20260426_163540`

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

Configuration	VRAM Required	Notes
F16 (this repo) · full GPU	~70 GB	53.8 GB model + KV cache
F16 · multi-GPU split	~18 GB per GPU	4× A10G 24 GB or 2× A100 40 GB
F16 · partial CPU offload	~40 GB VRAM + 32 GB RAM	Reduced context; slower inference
Q4_K_M (companion repo)	~22 GB	16.5 GB — single RTX 4090 or A10G

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 · 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 (required reading, UAT CSC 373)

📞 Work With PBH Applied Systems

👉 Book a Scoping Call · 👉 Request an Evaluation Report — from $2,500

Connect


🌐	pbhappliedsystems.com
📧	patrick@pbhappliedsystems.com
💼	LinkedIn
▶️	YouTube
📸	Instagram
👍	Facebook

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 · No behavioral evaluation — see companion Q4_K_M repository for all evaluation data

Downloads last month: 173

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

16-bit

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