Instructions to use vedantjadhav701/windows_troubleshooting_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vedantjadhav701/windows_troubleshooting_data 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 vedantjadhav701/windows_troubleshooting_data:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vedantjadhav701/windows_troubleshooting_data:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vedantjadhav701/windows_troubleshooting_data:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vedantjadhav701/windows_troubleshooting_data: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 vedantjadhav701/windows_troubleshooting_data:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vedantjadhav701/windows_troubleshooting_data: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 vedantjadhav701/windows_troubleshooting_data:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vedantjadhav701/windows_troubleshooting_data:Q4_K_M

Use Docker

docker model run hf.co/vedantjadhav701/windows_troubleshooting_data:Q4_K_M

LM Studio
Jan
Ollama
How to use vedantjadhav701/windows_troubleshooting_data with Ollama:
```
ollama run hf.co/vedantjadhav701/windows_troubleshooting_data:Q4_K_M
```
Unsloth Desktop

How to use vedantjadhav701/windows_troubleshooting_data with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vedantjadhav701/windows_troubleshooting_data:Q4_K_M

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": "vedantjadhav701/windows_troubleshooting_data:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vedantjadhav701/windows_troubleshooting_data with Docker Model Runner:
```
docker model run hf.co/vedantjadhav701/windows_troubleshooting_data:Q4_K_M
```

Lemonade

How to use vedantjadhav701/windows_troubleshooting_data with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vedantjadhav701/windows_troubleshooting_data:Q4_K_M

Run and chat with the model

lemonade run user.windows_troubleshooting_data-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vedantjadhav701/windows_troubleshooting_data with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vedantjadhav701/windows_troubleshooting_data: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 vedantjadhav701/windows_troubleshooting_data:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vedantjadhav701/windows_troubleshooting_data with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vedantjadhav701/windows_troubleshooting_data: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 "vedantjadhav701/windows_troubleshooting_data: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"

windows_troubleshooting_data / tokenizer.json

vedantjadhav701

(Trained with Unsloth)

600c987 verified 3 months ago

History Blame Contribute Delete

11.4 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 1e66b1f68202dd83bc47942692e5264b18d00411f298432676c73d0631505451
Size of remote file:: 11.4 MB
SHA256:: 8f56f60c102056ac818ef8b6645663ad9da827e1f572d53150be5ebd41bdd7df

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.