Instructions to use QuantFactory/Ahma-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Ahma-3B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Ahma-3B-GGUF", filename="Ahma-3B.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/Ahma-3B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Ahma-3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Ahma-3B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Ahma-3B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Ahma-3B-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 QuantFactory/Ahma-3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Ahma-3B-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 QuantFactory/Ahma-3B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Ahma-3B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Ahma-3B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Ahma-3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Ahma-3B-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": "QuantFactory/Ahma-3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/Ahma-3B-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/Ahma-3B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Ahma-3B-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/Ahma-3B-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 QuantFactory/Ahma-3B-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 QuantFactory/Ahma-3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Ahma-3B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Ahma-3B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Ahma-3B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Ahma-3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Ahma-3B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ahma-3B-GGUF-Q4_K_M
List all available models
lemonade list
Improve model card: Add GGUF usage, paper link, and correct metadata
#1
by nielsr HF Staff - opened
This PR improves the model card for the QuantFactory/Ahma-3B-GGUF model by:
- Adding
library_name: llama.cppto the metadata, which is appropriate for a GGUF artifact and enables the relevant "how to use" section. - Removing the incorrect
inference: falsemetadata tag. - Adding
ggufto the model tags for better discoverability. - Prominently linking to the paper "Scaling Data-Constrained Language Models", which influenced the training of the base
Ahma-3Bmodel. - Providing a clear usage example for the GGUF model using
llama-cpp-python. - Clarifying the existing
transformersusage section to indicate it is for the original (non-GGUF)Finnish-NLP/Ahma-3Bmodel. - Adding a direct link to the
llama.cppGitHub repository in the introduction.
These changes help users understand how to best utilize this specific GGUF model and provides clearer context for its development.
munish0838 changed pull request status to merged