Instructions to use sammbann/md-llama-finetune-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use sammbann/md-llama-finetune-v1 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 sammbann/md-llama-finetune-v1 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 sammbann/md-llama-finetune-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sammbann/md-llama-finetune-v1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="sammbann/md-llama-finetune-v1", max_seq_length=2048, )
md-llama-finetune-v1 : GGUF
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
- For text only LLMs:
llama-cli -hf sammbann/md-llama-finetune-v1 --jinja - For multimodal models:
llama-mtmd-cli -hf sammbann/md-llama-finetune-v1 --jinja
Available Model files:
Meta-Llama-3.1-8B-Instruct.Q6_K.gguf
Ollama
An Ollama Modelfile is included for easy deployment.
This was trained 2x faster with Unsloth

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