Instructions to use authbbag/tiny-llama_lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use authbbag/tiny-llama_lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("authbbag/tiny-llama_lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use authbbag/tiny-llama_lora_model 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 authbbag/tiny-llama_lora_model:F16 # Run inference directly in the terminal: llama cli -hf authbbag/tiny-llama_lora_model:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf authbbag/tiny-llama_lora_model:F16 # Run inference directly in the terminal: llama cli -hf authbbag/tiny-llama_lora_model: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 authbbag/tiny-llama_lora_model:F16 # Run inference directly in the terminal: ./llama-cli -hf authbbag/tiny-llama_lora_model: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 authbbag/tiny-llama_lora_model:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf authbbag/tiny-llama_lora_model:F16
Use Docker
docker model run hf.co/authbbag/tiny-llama_lora_model:F16
- LM Studio
- Jan
- Ollama
How to use authbbag/tiny-llama_lora_model with Ollama:
ollama run hf.co/authbbag/tiny-llama_lora_model:F16
- Unsloth Desktop
- Docker Model Runner
How to use authbbag/tiny-llama_lora_model with Docker Model Runner:
docker model run hf.co/authbbag/tiny-llama_lora_model:F16
- Lemonade
How to use authbbag/tiny-llama_lora_model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull authbbag/tiny-llama_lora_model:F16
Run and chat with the model
lemonade run user.tiny-llama_lora_model-F16
List all available models
lemonade list
- Atomic Chat
Download tiny-llama_lora_model-unsloth.F16.gguf from authbbag/tiny-llama_lora_model: direct link, hf CLI and curl.
- Browser
- Download file 2.2 GB
-
https://huggingface.co/authbbag/tiny-llama_lora_model/resolve/6092794d06f40621b8518657dfdb9253f3bb7a50/tiny-llama_lora_model-unsloth.F16.gguf
- Command line
-
hf download hf://authbbag/tiny-llama_lora_model@6092794d06f40621b8518657dfdb9253f3bb7a50/tiny-llama_lora_model-unsloth.F16.gguf
-
curl -L -o tiny-llama_lora_model-unsloth.F16.gguf https://huggingface.co/authbbag/tiny-llama_lora_model/resolve/6092794d06f40621b8518657dfdb9253f3bb7a50/tiny-llama_lora_model-unsloth.F16.gguf
2.2 GB
- Xet hash:
- ba36726f9ec050bf6de4908cef820f9eb140f4e4a91e8c2e6429d818c120f62d
- Size of remote file:
- 2.2 GB
- SHA256:
- 896b1acc3eb0aeead3bd4788c20c620988635039a9eceeee26c8fb43b60bbfbb
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