Instructions to use HarshG05/InfraGenie-Azure-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarshG05/InfraGenie-Azure-Expert with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HarshG05/InfraGenie-Azure-Expert", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HarshG05/InfraGenie-Azure-Expert 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 HarshG05/InfraGenie-Azure-Expert:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HarshG05/InfraGenie-Azure-Expert:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HarshG05/InfraGenie-Azure-Expert:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HarshG05/InfraGenie-Azure-Expert: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 HarshG05/InfraGenie-Azure-Expert:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HarshG05/InfraGenie-Azure-Expert: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 HarshG05/InfraGenie-Azure-Expert:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HarshG05/InfraGenie-Azure-Expert:Q4_K_M

Use Docker

docker model run hf.co/HarshG05/InfraGenie-Azure-Expert:Q4_K_M

LM Studio
Jan
Ollama
How to use HarshG05/InfraGenie-Azure-Expert with Ollama:
```
ollama run hf.co/HarshG05/InfraGenie-Azure-Expert:Q4_K_M
```

Unsloth Studio

How to use HarshG05/InfraGenie-Azure-Expert 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 HarshG05/InfraGenie-Azure-Expert 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 HarshG05/InfraGenie-Azure-Expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HarshG05/InfraGenie-Azure-Expert to start chatting

How to use HarshG05/InfraGenie-Azure-Expert with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HarshG05/InfraGenie-Azure-Expert: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": "HarshG05/InfraGenie-Azure-Expert:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use HarshG05/InfraGenie-Azure-Expert with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HarshG05/InfraGenie-Azure-Expert: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 "HarshG05/InfraGenie-Azure-Expert: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"

Docker Model Runner
How to use HarshG05/InfraGenie-Azure-Expert with Docker Model Runner:
```
docker model run hf.co/HarshG05/InfraGenie-Azure-Expert:Q4_K_M
```

Lemonade

How to use HarshG05/InfraGenie-Azure-Expert with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HarshG05/InfraGenie-Azure-Expert:Q4_K_M

Run and chat with the model

lemonade run user.InfraGenie-Azure-Expert-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use HarshG05/InfraGenie-Azure-Expert with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HarshG05/InfraGenie-Azure-Expert: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 HarshG05/InfraGenie-Azure-Expert:Q4_K_M

Run Hermes

hermes

Atomic Chat

InfraGenie-Azure-Expert / .gitattributes

Commit History

Upload infragenie-q4_k_m.gguf with huggingface_hub

0bc0c9b
verified

HarshG05 commited on 16 days ago

Upload model trained with Unsloth

ee3474b
verified

HarshG05 commited on 16 days ago

initial commit

d28a77c
verified

HarshG05 commited on 16 days ago

Commit History

Upload infragenie-q4_k_m.gguf with huggingface_hub 0bc0c9b verified

Upload model trained with Unsloth ee3474b verified

initial commit d28a77c verified

Upload infragenie-q4_k_m.gguf with huggingface_hub

0bc0c9b
verified

Upload model trained with Unsloth

ee3474b
verified

initial commit

d28a77c
verified