Instructions to use Florents-Tselai/Meltemi-llamafile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Florents-Tselai/Meltemi-llamafile with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Florents-Tselai/Meltemi-llamafile", filename="Meltemi-7B-Instruct-v1.5-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Florents-Tselai/Meltemi-llamafile with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Florents-Tselai/Meltemi-llamafile:F16 # Run inference directly in the terminal: llama-cli -hf Florents-Tselai/Meltemi-llamafile:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Florents-Tselai/Meltemi-llamafile:F16 # Run inference directly in the terminal: llama-cli -hf Florents-Tselai/Meltemi-llamafile:F16
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 Florents-Tselai/Meltemi-llamafile:F16 # Run inference directly in the terminal: ./llama-cli -hf Florents-Tselai/Meltemi-llamafile:F16
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 Florents-Tselai/Meltemi-llamafile:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Florents-Tselai/Meltemi-llamafile:F16
Use Docker
docker model run hf.co/Florents-Tselai/Meltemi-llamafile:F16
- LM Studio
- Jan
- vLLM
How to use Florents-Tselai/Meltemi-llamafile with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Florents-Tselai/Meltemi-llamafile" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Florents-Tselai/Meltemi-llamafile", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Florents-Tselai/Meltemi-llamafile:F16
- Ollama
How to use Florents-Tselai/Meltemi-llamafile with Ollama:
ollama run hf.co/Florents-Tselai/Meltemi-llamafile:F16
- Unsloth Studio
How to use Florents-Tselai/Meltemi-llamafile 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 Florents-Tselai/Meltemi-llamafile 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 Florents-Tselai/Meltemi-llamafile to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Florents-Tselai/Meltemi-llamafile to start chatting
- Docker Model Runner
How to use Florents-Tselai/Meltemi-llamafile with Docker Model Runner:
docker model run hf.co/Florents-Tselai/Meltemi-llamafile:F16
- Lemonade
How to use Florents-Tselai/Meltemi-llamafile with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Florents-Tselai/Meltemi-llamafile:F16
Run and chat with the model
lemonade run user.Meltemi-llamafile-F16
List all available models
lemonade list
Meltemi llamafile & gguf
This repo contains llamafile and gguf file format models for Meltemi 7B Instruct v1.5, the first Greek Large Language Model (LLM),
trained by the Institute for Language and Speech Processing at Athena Research & Innovation Center.
lamafile is a file format introduced by Mozilla Ocho on Nov 20th 2023, and it collapses the complexity of an LLM into a single executable file. This gives you the easiest, fastest way to use Meltemi on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD systems you control on both AMD64 and ARM64.
It's as simple as this
wget https://huggingface.co/Florents-Tselai/Meltemi-llamafile/resolve/main/Meltemi-7B-Instruct-v1.5-Q8_0.llamafile
chmod +x Meltemi-7B-Instruct-v1.5-Q8_0.llamafile
./Meltemi-7B-Instruct-v1.5-Q8_0.llamafile
This will open a tab with a chatbot and completion interface in your browser.
For additional help on how it may be used, pass the --help flag.
API
The server also has an OpenAI API-compatible completions endpoint.
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"model": "LLaMA_CPP",
"messages": [
{
"role": "system",
"content": "Είσαι ένας φωτεινός παντογνώστης"
},
{
"role": "user",
"content": "Γράψε μου μια ιστορία για έναν βάτραχο που έγινε αρνάκι"
}
]
}'
CLI
An advanced CLI mode is provided that's useful for shell scripting.
You can use it by passing the --cli flag. For additional help on how it may be used, pass the --help flag.
./Meltemi-7B-Instruct-v1.5-Q8_0.llamafile -p 'Ποιό είναι το νόημα της ζωής;'
To see all available options
./Meltemi-7B-Instruct-v1.5-Q8_0.llamafile --help
gguf
gguf file formats are also available if you're working with llama.cpp llama.cpp
llama.cpp offers quite a lot of options, thus refer to its documentation.
Basic Usage
llama-cli -m ./Meltemi-7B-Instruct-v1.5-F16.gguf -p "Ποιό είναι το νόημα της ζωής;" -n 128
Conversation Mode
llama-cli -m ./Meltemi-7B-Instruct-v1.5-F16.gguf --conv
Web Server
llama-server -m ./Meltemi-7B-Instruct-v1.5-F16.gguf --port 8080
Model Information
- Vocabulary extension of the Mistral 7b tokenizer with Greek tokens for lower costs and faster inference (1.52 vs. 6.80 tokens/word for Greek)
- 8192 context length
For more details, please refer to the original model card Meltemi 7B Instract v1.5
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Model tree for Florents-Tselai/Meltemi-llamafile
Base model
ilsp/Meltemi-7B-Instruct-v1.5