Text Generation
Transformers
GGUF
English
dnotitia
nlp
llm
conversation
chat
reasoning
TensorBlock
GGUF
conversational
Instructions to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF 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 tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
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 tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
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 tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
- SGLang
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Ollama:
ollama run hf.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
- Unsloth Desktop
- Pi
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
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": "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
- Lemonade
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
Run and chat with the model
lemonade run user.dnotitia_Smoothie-Qwen3-30B-A3B-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
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 tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K
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 "tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF:Q2_K" \ --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"
File size: 7,397 Bytes
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language:
- en
license: apache-2.0
tags:
- dnotitia
- nlp
- llm
- conversation
- chat
- reasoning
- TensorBlock
- GGUF
base_model: dnotitia/Smoothie-Qwen3-30B-A3B
library_name: transformers
pipeline_tag: text-generation
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## dnotitia/Smoothie-Qwen3-30B-A3B - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building β
</a>
</div>
This repo contains GGUF format model files for [dnotitia/Smoothie-Qwen3-30B-A3B](https://huggingface.co/dnotitia/Smoothie-Qwen3-30B-A3B).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π Try it now! π</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
</tr>
</table>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Smoothie-Qwen3-30B-A3B-Q2_K.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q2_K.gguf) | Q2_K | 11.259 GB | smallest, significant quality loss - not recommended for most purposes |
| [Smoothie-Qwen3-30B-A3B-Q3_K_S.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q3_K_S.gguf) | Q3_K_S | 13.292 GB | very small, high quality loss |
| [Smoothie-Qwen3-30B-A3B-Q3_K_M.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q3_K_M.gguf) | Q3_K_M | 14.712 GB | very small, high quality loss |
| [Smoothie-Qwen3-30B-A3B-Q3_K_L.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q3_K_L.gguf) | Q3_K_L | 15.901 GB | small, substantial quality loss |
| [Smoothie-Qwen3-30B-A3B-Q4_0.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q4_0.gguf) | Q4_0 | 17.304 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Smoothie-Qwen3-30B-A3B-Q4_K_S.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q4_K_S.gguf) | Q4_K_S | 17.456 GB | small, greater quality loss |
| [Smoothie-Qwen3-30B-A3B-Q4_K_M.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q4_K_M.gguf) | Q4_K_M | 18.557 GB | medium, balanced quality - recommended |
| [Smoothie-Qwen3-30B-A3B-Q5_0.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q5_0.gguf) | Q5_0 | 21.081 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Smoothie-Qwen3-30B-A3B-Q5_K_S.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q5_K_S.gguf) | Q5_K_S | 21.081 GB | large, low quality loss - recommended |
| [Smoothie-Qwen3-30B-A3B-Q5_K_M.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q5_K_M.gguf) | Q5_K_M | 21.726 GB | large, very low quality loss - recommended |
| [Smoothie-Qwen3-30B-A3B-Q6_K.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q6_K.gguf) | Q6_K | 25.093 GB | very large, extremely low quality loss |
| [Smoothie-Qwen3-30B-A3B-Q8_0.gguf](https://huggingface.co/tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF/blob/main/Smoothie-Qwen3-30B-A3B-Q8_0.gguf) | Q8_0 | 32.484 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF --include "Smoothie-Qwen3-30B-A3B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/dnotitia_Smoothie-Qwen3-30B-A3B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|