Instructions to use alex0101010101/lora_model-F16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex0101010101/lora_model-F16-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alex0101010101/lora_model-F16-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use alex0101010101/lora_model-F16-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 alex0101010101/lora_model-F16-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 alex0101010101/lora_model-F16-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for alex0101010101/lora_model-F16-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="alex0101010101/lora_model-F16-GGUF", max_seq_length=2048, )
metadata
base_model: alex0101010101/lora_model
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
alex0101010101/lora_model-F16-GGUF
This LoRA adapter was converted to GGUF format from alex0101010101/lora_model via the ggml.ai's GGUF-my-lora space.
Refer to the original adapter repository for more details.
Use with llama.cpp
# with cli
llama-cli -m base_model.gguf --lora lora_model-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora lora_model-f16.gguf (...other args)
To know more about LoRA usage with llama.cpp server, refer to the llama.cpp server documentation.