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 23

Commit

07a4dc7

verified ·

1 Parent(s): 5d0ccf6

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +5 -53

README.md CHANGED Viewed

@@ -24,23 +24,20 @@ language:
 ## TQ3_4S Release
-This repository packages the model as a TurboQuant `TQ3_4S` GGUF for local deployment. It can be used in two modes:
-- **Text-only chat / coding:** use `Qwen3.6-27B-TQ3_4S.gguf` only.
-- **Image-to-text / multimodal:** use `Qwen3.6-27B-TQ3_4S.gguf` together with `mmproj.gguf`.
 ## Runtime Compatibility
 This quant requires a TurboQuant-capable runtime. For llama.cpp, use the `turbo-tan/llama.cpp-tq3` fork rather than stock upstream llama.cpp if you want native `TQ3_4S` support.
 - TurboQuant runtime fork: [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3)
 ## Files
 | File | Quant | Size |
 | --- | --- | ---: |
-| `Qwen3.6-27B-TQ3_4S.gguf` | TQ3_4S text model | ~13.0 GB |
-| `mmproj.gguf` | Qwen3.6-27B vision projector | ~889 MB |
 | `chat_template.jinja` | chat template | text |
 | `thumbnail.png` | model card image | png |
@@ -66,44 +63,11 @@ Prompt processing:
 - Use a TurboQuant-capable llama.cpp build for best performance.
 - For llama.cpp, the intended runtime is the `turbo-tan/llama.cpp-tq3` fork.
-- Text-only usage does not need `mmproj.gguf`.
-- Image-to-text usage requires `mmproj.gguf`; pass it with `--mmproj mmproj.gguf` when using `llama-server` or other compatible llama.cpp tools.
 - 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.
-## Text-Only vs Image-To-Text
-### Text-only
-For normal chat, coding, and text generation, load only the main model:
-```bash
-llama-server \
-  -m Qwen3.6-27B-TQ3_4S.gguf \
-  -ngl 99 -c 4096 -np 1 \
-  -ctk q4_0 -ctv tq3_0 -fa on \
-  --jinja --reasoning off --reasoning-budget 0
-```
-### Image-to-text
-For vision/image prompts, also load the projector:
-`mmproj.gguf` was smoke-tested with the `turbo-tan/llama.cpp-tq3` `llama-server` runtime on RTX 5060 Ti. The server loaded the projector as a Qwen-VL multimodal model and `/health` returned `ok`.
-Validated smoke-test settings:
-```bash
-llama-server \
-  -m Qwen3.6-27B-TQ3_4S.gguf \
-  --mmproj mmproj.gguf \
-  -ngl 99 -c 2048 -np 1 \
-  -ctk q4_0 -ctv tq3_0 -fa on \
-  --jinja --reasoning off --reasoning-budget 0
-```
 ## Recommended llama.cpp Settings
 Default prompt-processing settings on 16 GB:
@@ -118,23 +82,11 @@ llama-bench \
   -p 2048 -n 0 -r 3
 ```
-Default text-only chat/server settings:
-```bash
-llama-server \
-  -m Qwen3.6-27B-TQ3_4S.gguf \
-  --host 127.0.0.1 --port 8080 \
-  -ngl 99 -c 4096 -np 1 \
-  -ctk q4_0 -ctv tq3_0 -fa on \
-  --jinja
-```
-Image-to-text server settings:
 ```bash
 llama-server \
   -m Qwen3.6-27B-TQ3_4S.gguf \
-  --mmproj mmproj.gguf \
   --host 127.0.0.1 --port 8080 \
   -ngl 99 -c 4096 -np 1 \
   -ctk q4_0 -ctv tq3_0 -fa on \

 ## TQ3_4S Release
+This repository packages the model as a TurboQuant `TQ3_4S` GGUF for local deployment.
 ## Runtime Compatibility
 This quant requires a TurboQuant-capable runtime. For llama.cpp, use the `turbo-tan/llama.cpp-tq3` fork rather than stock upstream llama.cpp if you want native `TQ3_4S` support.
 - TurboQuant runtime fork: [turbo-tan/llama.cpp-tq3](https://github.com/turbo-tan/llama.cpp-tq3)
+- LM Studio setup: [docs/backend/LMStudio.md](https://github.com/turbo-tan/llama.cpp-tq3/blob/main/docs/backend/LMStudio.md)
 ## 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 |
 - Use a TurboQuant-capable llama.cpp build for best performance.
 - For llama.cpp, the intended runtime is the `turbo-tan/llama.cpp-tq3` fork.
+- 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.
 ## Recommended llama.cpp Settings
 Default prompt-processing settings on 16 GB:
   -p 2048 -n 0 -r 3
 ```
+Default chat/server settings:
 ```bash
 llama-server \
   -m Qwen3.6-27B-TQ3_4S.gguf \
   --host 127.0.0.1 --port 8080 \
   -ngl 99 -c 4096 -np 1 \
   -ctk q4_0 -ctv tq3_0 -fa on \