@coffeecup2020: TurboQuant - Qwopus3.6-27B-v2-TQ3_4S.gguf Confirmed with gpqa test this is something great. https://huggingface.co/YTan…

X AI KOLs Timeline Models

Summary

TurboQuant is a GGUF quantized version of the Qwopus3.6-27B-v2 model, confirmed with GPQA test results and shared on Hugging Face, with credits to Jackrong and KyleHessling.

TurboQuant - Qwopus3.6-27B-v2-TQ3_4S.gguf Confirmed with gpqa test this is something great. https://huggingface.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S?v… Though 0 donation, a like in HF and credit to Jackrong and @KyleHessling will be great. I am only doing minumum work. These guys has made this for free by their hard work!
Original Article
View Cached Full Text

Cached at: 05/24/26, 08:27 AM

TurboQuant - Qwopus3.6-27B-v2-TQ3_4S.gguf Confirmed with gpqa test this is something great.

https://huggingface.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S?v…

Though 0 donation, a like in HF and credit to Jackrong and @KyleHessling will be great. I am only doing minumum work. These guys has made this for free by their hard work!


YTan2000/Qwopus3.6-27B-v2-TQ3_4S · Hugging Face

Source: https://huggingface.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S?v Librariesllama-cpp-pythonHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YTan2000/Qwopus3.6-27B-v2-TQ3_4S",
	filename="Qwopus3.6-27B-v2-TQ3_4S.gguf",
)
llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

NotebooksGoogle ColabKaggleLocal Appshttps://huggingface.co/settings/local-apps#local-appsllama.cppHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with llama.cpp:

Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
# Run inference directly in the terminal:
llama-cli -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
# Run inference directly in the terminal:
llama-cli -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
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 YTan2000/Qwopus3.6-27B-v2-TQ3_4S
# Run inference directly in the terminal:
./llama-cli -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
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 YTan2000/Qwopus3.6-27B-v2-TQ3_4S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
Use Docker
docker model run hf.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S

LM StudioJanvLLMHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with vLLM:

Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YTan2000/Qwopus3.6-27B-v2-TQ3_4S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YTan2000/Qwopus3.6-27B-v2-TQ3_4S",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'
Use Docker
docker model run hf.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S

OllamaHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with Ollama:

ollama run hf.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S

Unsloth StudionewHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S 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 YTan2000/Qwopus3.6-27B-v2-TQ3_4S 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 YTan2000/Qwopus3.6-27B-v2-TQ3_4S to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YTan2000/Qwopus3.6-27B-v2-TQ3_4S to start chatting

PinewHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with Pi:

Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
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": "YTan2000/Qwopus3.6-27B-v2-TQ3_4S"
        }
      ]
    }
  }
}
Run Pi
# Start Pi in your project directory:
pi

Hermes AgentnewHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with Hermes Agent:

Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf YTan2000/Qwopus3.6-27B-v2-TQ3_4S
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 YTan2000/Qwopus3.6-27B-v2-TQ3_4S
Run Hermes
hermes

Docker Model RunnerHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with Docker Model Runner:

docker model run hf.co/YTan2000/Qwopus3.6-27B-v2-TQ3_4S

LemonadeHow to use YTan2000/Qwopus3.6-27B-v2-TQ3_4S with Lemonade:

Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull YTan2000/Qwopus3.6-27B-v2-TQ3_4S
Run and chat with the model
lemonade run user.Qwopus3.6-27B-v2-TQ3_4S-{{QUANT_TAG}}
List all available models
lemonade list

Similar Articles

Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF

Hugging Face Models Trending

A GGUF quantized version of the Qwopus3.6-27B-Coder-MTP model is released on Hugging Face, optimized for local inference and compatible with Transformers, vLLM, SGLang, and Unsloth Studio.

Jackrong/Qwopus3.6-27B-v2-MTP-GGUF

Hugging Face Models Trending

Jackrong/Qwopus3.6-27B-v2-MTP-GGUF is a quantized GGUF version of a 27B parameter language model, hosted on Hugging Face with instructions for use with various libraries and tools.

Jackrong/Qwopus3.6-27B-v2-GGUF

Hugging Face Models Trending

Qwopus3.6-27B-v2 is a reasoning-enhanced fine-tuned version of Qwen3.6-27B, using Trace Inversion datasets and curriculum learning, released as GGUF for efficient inference.