Instructions to use nold/HelpingAI-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nold/HelpingAI-9B-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 nold/HelpingAI-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf nold/HelpingAI-9B-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 nold/HelpingAI-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf nold/HelpingAI-9B-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 nold/HelpingAI-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf nold/HelpingAI-9B-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 nold/HelpingAI-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf nold/HelpingAI-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/nold/HelpingAI-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use nold/HelpingAI-9B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nold/HelpingAI-9B-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": "nold/HelpingAI-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nold/HelpingAI-9B-GGUF:Q4_K_M
- Ollama
How to use nold/HelpingAI-9B-GGUF with Ollama:
ollama run hf.co/nold/HelpingAI-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use nold/HelpingAI-9B-GGUF with Docker Model Runner:
docker model run hf.co/nold/HelpingAI-9B-GGUF:Q4_K_M
- Lemonade
How to use nold/HelpingAI-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nold/HelpingAI-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.HelpingAI-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download test.log from nold/HelpingAI-9B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 722 Bytes
-
https://huggingface.co/nold/HelpingAI-9B-GGUF/resolve/0aded2f175415b134a4451102eaf3432dec05db0/test.log
- Command line
-
hf download hf://nold/HelpingAI-9B-GGUF@0aded2f175415b134a4451102eaf3432dec05db0/test.log
-
curl -L -o test.log https://huggingface.co/nold/HelpingAI-9B-GGUF/resolve/0aded2f175415b134a4451102eaf3432dec05db0/test.log
722 Bytes
| What is a Large Language Model? | |
| Large Language Models (LLMs) are AI models that have been trained on a massive amount of text data, enabling them to generate human-like responses and perform tasks such as translation, summarization, and text generation. These models are capable of understanding and processing complex language patterns, making them highly effective in a wide range of natural language processing tasks. | |
| LLMs have been developed to assist with various language-related tasks, such as language translation, text summarization, and generating responses to user queries. They have been trained on large datasets to learn from the data, enabling them to generate human-like responses and perform tasks with | |