Instructions to use YTan2000/Qwen3.6-27B-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use YTan2000/Qwen3.6-27B-TQ3_4S 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 YTan2000/Qwen3.6-27B-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf YTan2000/Qwen3.6-27B-TQ3_4S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YTan2000/Qwen3.6-27B-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf YTan2000/Qwen3.6-27B-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/Qwen3.6-27B-TQ3_4S
# Run inference directly in the terminal:
./llama-cli -hf YTan2000/Qwen3.6-27B-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/Qwen3.6-27B-TQ3_4S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YTan2000/Qwen3.6-27B-TQ3_4S

Use Docker

docker model run hf.co/YTan2000/Qwen3.6-27B-TQ3_4S

LM Studio
Jan

vLLM

How to use YTan2000/Qwen3.6-27B-TQ3_4S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YTan2000/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-TQ3_4S

Ollama
How to use YTan2000/Qwen3.6-27B-TQ3_4S with Ollama:
```
ollama run hf.co/YTan2000/Qwen3.6-27B-TQ3_4S
```
Unsloth Desktop

How to use YTan2000/Qwen3.6-27B-TQ3_4S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Qwen3.6-27B-TQ3_4S

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": "YTan2000/Qwen3.6-27B-TQ3_4S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use YTan2000/Qwen3.6-27B-TQ3_4S with Docker Model Runner:
```
docker model run hf.co/YTan2000/Qwen3.6-27B-TQ3_4S
```

Lemonade

How to use YTan2000/Qwen3.6-27B-TQ3_4S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YTan2000/Qwen3.6-27B-TQ3_4S

Run and chat with the model

lemonade run user.Qwen3.6-27B-TQ3_4S-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use YTan2000/Qwen3.6-27B-TQ3_4S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Qwen3.6-27B-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/Qwen3.6-27B-TQ3_4S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YTan2000/Qwen3.6-27B-TQ3_4S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Qwen3.6-27B-TQ3_4S

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 "YTan2000/Qwen3.6-27B-TQ3_4S" \
  --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"

YTan2000 commited on Apr 22

Commit

3798cdd

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1 Parent(s): 9766a5c

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	@@ -0,0 +1,103 @@

+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen3.6-27B
+tags:
+  - qwen3.6
+  - gguf
+  - tq3_4s
+  - turboquant
+  - vision
+  - multimodal
+pipeline_tag: image-text-to-text
+language:
+  - en
+  - zh
+  - multilingual
+---
+# Qwen3.6-27B-TQ3_4S
+TurboQuant `TQ3_4S` GGUF release of Qwen3.6-27B for llama.cpp-style runtimes.
+This release is based on the `unsloth/Qwen3.6-27B-GGUF` conversion path and packaged for local GGUF inference with TurboQuant support.
+Following the Qwen3.5 series, Qwen describes Qwen3.6-27B as the first open-weight Qwen3.6 variant, aimed at better stability and real-world coding utility. The upstream model card highlights stronger agentic coding and better preservation of reasoning context across turns.
+## Qwen3.6 Base Model Highlights
+- Agentic coding focus, including repo-level reasoning and frontend workflows
+- Reasoning-context preservation across historical turns
+- Native multimodal family support
+- Native `262,144` context, with longer-context extension support in upstream frameworks
+## Base Model Overview
+- Architecture: `qwen35`
+- Parameters: `27B`
+- Layers: `64`
+- Embedding dimension: `5120`
+- FFN dimension: `17408`
+- Hidden layout: `16 × (3 × (Gated DeltaNet -> FFN) -> 1 × (Gated Attention -> FFN))`
+- Gated DeltaNet heads: `48` for `V`, `16` for `QK`, head dim `128`
+- Gated Attention heads: `24` for `Q`, `4` for `KV`, head dim `256`
+- RoPE dim: `64`
+- Native context: `262,144`
+## Files
+| File | Quant | Size |
+| --- | --- | ---: |
+| `Qwen3.6-27B-TQ3_4S.gguf` | TQ3_4S | ~13.0 GB |
+| `chat_template.jinja` | chat template | text |
+| `thumbnail.png` | model card image | png |
+## Local Validation
+Hardware:
+- RTX 5060 Ti 16 GB
+Prompt processing:
+- `llama-perplexity --chunks 10 -c 2048`
+- `PPL = 6.2452 +/- 0.16138`
+- `prompt eval = 712.02 tok/s`
+## Selected Upstream Benchmark Highlights
+The upstream Qwen model card reports the following headline numbers for the base `Qwen3.6-27B` model:
+- `SWE-bench Verified`: `77.2`
+- `Terminal-Bench 2.0`: `59.3`
+- `SkillsBench Avg5`: `48.2`
+- `GPQA Diamond`: `87.8`
+- `AIME26`: `94.1`
+- `MMMU`: `82.9`
+- `AndroidWorld`: `70.3`
+These are upstream base-model results, not local GGUF quant results.
+## Runtime Notes
+- Use a TurboQuant-capable llama.cpp build for best performance.
+- The upstream family is multimodal-capable, but the public 27B repos used here do not currently expose a separate GGUF `mmproj` artifact.
+- For llama.cpp chat usage, keep `--jinja` enabled so the bundled chat template is honored.
+- Upstream guidance recommends keeping at least `128K` context when possible for reasoning-heavy workloads. On smaller local GPUs, reduce context as needed to fit memory.
+- Upstream default sampling guidance differs between thinking and non-thinking mode; follow the official Qwen card if you are trying to reproduce base-model behavior.
+## Example
+```bash
+llama-cli \
+  -m Qwen3.6-27B-TQ3_4S.gguf \
+  --jinja \
+  -ngl 99 \
+  -c 4096
+```
+## Sources
+- Upstream base model: [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)
+- Upstream GGUF source used for conversion: [unsloth/Qwen3.6-27B-GGUF](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
+- Upstream blog and benchmark context: [Qwen3.6-27B model card](https://huggingface.co/Qwen/Qwen3.6-27B)