Instructions to use mradermacher/DataSense-GPU-Full-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/DataSense-GPU-Full-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/DataSense-GPU-Full-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/DataSense-GPU-Full-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 mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

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 mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

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 mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/DataSense-GPU-Full-GGUF with Ollama:
```
ollama run hf.co/mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/DataSense-GPU-Full-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 mradermacher/DataSense-GPU-Full-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 mradermacher/DataSense-GPU-Full-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/DataSense-GPU-Full-GGUF to start chatting

How to use mradermacher/DataSense-GPU-Full-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

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": "mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/DataSense-GPU-Full-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/DataSense-GPU-Full-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DataSense-GPU-Full-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/DataSense-GPU-Full-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 mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

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 mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/DataSense-GPU-Full-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M

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 "mradermacher/DataSense-GPU-Full-GGUF:Q4_K_M" \
  --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"

mradermacher commited on Jul 5

Commit

2b529e6

verified ·

1 Parent(s): f6cf6d7

uploaded from nico1

Browse files

Files changed (13) hide show

.gitattributes +12 -0
DataSense-GPU-Full.IQ4_XS.gguf +3 -0
DataSense-GPU-Full.Q2_K.gguf +3 -0
DataSense-GPU-Full.Q3_K_L.gguf +3 -0
DataSense-GPU-Full.Q3_K_M.gguf +3 -0
DataSense-GPU-Full.Q3_K_S.gguf +3 -0
DataSense-GPU-Full.Q4_K_M.gguf +3 -0
DataSense-GPU-Full.Q4_K_S.gguf +3 -0
DataSense-GPU-Full.Q5_K_M.gguf +3 -0
DataSense-GPU-Full.Q5_K_S.gguf +3 -0
DataSense-GPU-Full.Q6_K.gguf +3 -0
DataSense-GPU-Full.Q8_0.gguf +3 -0
DataSense-GPU-Full.f16.gguf +3 -0

@@ -35,3 +35,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 DataSense-GPU-Full.mmproj-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 DataSense-GPU-Full.mmproj-f16.gguf filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 DataSense-GPU-Full.mmproj-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 DataSense-GPU-Full.mmproj-f16.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+DataSense-GPU-Full.f16.gguf filter=lfs diff=lfs merge=lfs -text

+version https://git-lfs.github.com/spec/v1
+oid sha256:afdd9398d0e9f62a78575415deb87c2482b6f925b08f59bce2a15a84fad10733
+size 3309828256

+version https://git-lfs.github.com/spec/v1
+oid sha256:18bf88f1273ad4f6cd5173a8346b01fb12a705f90484b907957ce51d6bb8360b
+size 2989086880

+version https://git-lfs.github.com/spec/v1
+oid sha256:0a30725880a0168c55eabfd241985958d4a035b86d6f4e013c802edf06d1f603
+size 3282352288

+version https://git-lfs.github.com/spec/v1
+oid sha256:18b38ba9bd6b8232d5fa0085d14e5af8a7f59a6d0e13dc74305ca9395ffd98fc
+size 3201349792

+version https://git-lfs.github.com/spec/v1
+oid sha256:13d6477d95c0023133e255e3569686d39cee5080d0874270de204d57e95356c5
+size 3110215840

+version https://git-lfs.github.com/spec/v1
+oid sha256:3f68300a5f10da01751f792024b438e5307408c3679ba7356acc19e334c3a707
+size 3427879072

+version https://git-lfs.github.com/spec/v1
+oid sha256:fe31442f22e063a09115d9a7f05e79eccd726ab3925f42293db2ba8b2ff0f2ad
+size 3365075104

+version https://git-lfs.github.com/spec/v1
+oid sha256:3f85926f8cf6bea89d1536351cd61f9e267ad56f9f9c31bdb7df4b69521c0051
+size 3630287008

+version https://git-lfs.github.com/spec/v1
+oid sha256:362ea917bf7d7f7f4056e2c7db677d9336306182d8fe56f0aa831cab74c455f6
+size 3595401376

+version https://git-lfs.github.com/spec/v1
+oid sha256:7d5dedc8bdb15a7a48145da660e8d649f996e1abaddaa3763baab3132db71f75
+size 3845345440

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:58ce49f24e764d461d53f479a114b289892ef3af8f897717da8b309f6a1e4818
+size 4967495840

+version https://git-lfs.github.com/spec/v1
+oid sha256:828aebfef402ee46d2e5929d6cbb61316466c9784b0242f0c5a8168e51092c34
+size 9311303840