Instructions to use tensorblock/DataVortexS-10.7B-v0.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tensorblock/DataVortexS-10.7B-v0.3-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/DataVortexS-10.7B-v0.3-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/DataVortexS-10.7B-v0.3-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/DataVortexS-10.7B-v0.3-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/DataVortexS-10.7B-v0.3-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/DataVortexS-10.7B-v0.3-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/DataVortexS-10.7B-v0.3-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/DataVortexS-10.7B-v0.3-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/DataVortexS-10.7B-v0.3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/DataVortexS-10.7B-v0.3-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/DataVortexS-10.7B-v0.3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K

Ollama
How to use tensorblock/DataVortexS-10.7B-v0.3-GGUF with Ollama:
```
ollama run hf.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/DataVortexS-10.7B-v0.3-GGUF 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 tensorblock/DataVortexS-10.7B-v0.3-GGUF 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 tensorblock/DataVortexS-10.7B-v0.3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/DataVortexS-10.7B-v0.3-GGUF to start chatting

Docker Model Runner
How to use tensorblock/DataVortexS-10.7B-v0.3-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K
```

Lemonade

How to use tensorblock/DataVortexS-10.7B-v0.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/DataVortexS-10.7B-v0.3-GGUF:Q2_K

Run and chat with the model

lemonade run user.DataVortexS-10.7B-v0.3-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

DataVortexS-10.7B-v0.3-GGUF / README.md

morriszms

Upload folder using huggingface_hub

be101d0 verified over 1 year ago

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	---
	tags:
	- text-generation
	- TensorBlock
	- GGUF
	license: cc-by-nc-sa-4.0
	language:
	- ko
	base_model: Edentns/DataVortexS-10.7B-v0.3
	pipeline_tag: text-generation
	datasets:
	- jojo0217/korean_rlhf_dataset
	---

	<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>
	<div style="display: flex; justify-content: space-between; width: 100%;">
	<div style="display: flex; flex-direction: column; align-items: flex-start;">
	<p style="margin-top: 0.5em; margin-bottom: 0em;">
	Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
	</p>
	</div>
	</div>

	## Edentns/DataVortexS-10.7B-v0.3 - GGUF

	This repo contains GGUF format model files for [Edentns/DataVortexS-10.7B-v0.3](https://huggingface.co/Edentns/DataVortexS-10.7B-v0.3).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

	## Prompt template

	```
	{system_prompt}

	### Instruction:
	{prompt}

	### Response:
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [DataVortexS-10.7B-v0.3-Q2_K.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q2_K.gguf) \| Q2_K \| 3.799 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [DataVortexS-10.7B-v0.3-Q3_K_S.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q3_K_S.gguf) \| Q3_K_S \| 4.421 GB \| very small, high quality loss \|
	\| [DataVortexS-10.7B-v0.3-Q3_K_M.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q3_K_M.gguf) \| Q3_K_M \| 4.916 GB \| very small, high quality loss \|
	\| [DataVortexS-10.7B-v0.3-Q3_K_L.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q3_K_L.gguf) \| Q3_K_L \| 5.339 GB \| small, substantial quality loss \|
	\| [DataVortexS-10.7B-v0.3-Q4_0.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q4_0.gguf) \| Q4_0 \| 5.740 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [DataVortexS-10.7B-v0.3-Q4_K_S.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q4_K_S.gguf) \| Q4_K_S \| 5.783 GB \| small, greater quality loss \|
	\| [DataVortexS-10.7B-v0.3-Q4_K_M.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q4_K_M.gguf) \| Q4_K_M \| 6.103 GB \| medium, balanced quality - recommended \|
	\| [DataVortexS-10.7B-v0.3-Q5_0.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q5_0.gguf) \| Q5_0 \| 6.982 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [DataVortexS-10.7B-v0.3-Q5_K_S.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q5_K_S.gguf) \| Q5_K_S \| 6.982 GB \| large, low quality loss - recommended \|
	\| [DataVortexS-10.7B-v0.3-Q5_K_M.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q5_K_M.gguf) \| Q5_K_M \| 7.169 GB \| large, very low quality loss - recommended \|
	\| [DataVortexS-10.7B-v0.3-Q6_K.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q6_K.gguf) \| Q6_K \| 8.301 GB \| very large, extremely low quality loss \|
	\| [DataVortexS-10.7B-v0.3-Q8_0.gguf](https://huggingface.co/tensorblock/DataVortexS-10.7B-v0.3-GGUF/tree/main/DataVortexS-10.7B-v0.3-Q8_0.gguf) \| Q8_0 \| 10.751 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/DataVortexS-10.7B-v0.3-GGUF --include "DataVortexS-10.7B-v0.3-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/DataVortexS-10.7B-v0.3-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```