Instructions to use Undi95/MLewd-L2-13B-v2-2-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 Undi95/MLewd-L2-13B-v2-2-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 Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
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 Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
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 Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
Use Docker
docker model run hf.co/Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use Undi95/MLewd-L2-13B-v2-2-GGUF with Ollama:
ollama run hf.co/Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
- Unsloth Desktop
- Docker Model Runner
How to use Undi95/MLewd-L2-13B-v2-2-GGUF with Docker Model Runner:
docker model run hf.co/Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
- Lemonade
How to use Undi95/MLewd-L2-13B-v2-2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Undi95/MLewd-L2-13B-v2-2-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.MLewd-L2-13B-v2-2-GGUF-Q4_K_S
List all available models
lemonade list
- Atomic Chat
Maximum context
What is the maximum context of this model? 2048? 4096? Or can absolutely any quantity be specified?
When running this model I am receiving following:
llm_load_print_meta: format = GGUF V2 (latest)
llm_load_print_meta: n_ctx_train = 4096
...
That means that this model performs best if context is set to 4096 . You are still able to extend the context window with this model though.
I have tested extending context to 16K using rope (parameters in llama.cpp in case you using this software) to summarize longer articles and it worked fine:
llm_load_print_meta: format = GGUF V2 (latest)
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_ctx = 16384
....
But overall, it is probably best to stick with 4K context window to avoid degradation and higher memory requirements when using this model for higher context window utilizing rope extension.