udanet/udanet_stambecco_data_it
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How to use udanet/udanet_stambecco_llama-3.1-8b_lora with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("udanet/udanet_stambecco_llama-3.1-8b_lora", device_map="auto")How to use udanet/udanet_stambecco_llama-3.1-8b_lora with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0 # Run inference directly in the terminal: llama cli -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0 # Run inference directly in the terminal: llama cli -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
docker model run hf.co/udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
How to use udanet/udanet_stambecco_llama-3.1-8b_lora with Ollama:
ollama run hf.co/udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
How to use udanet/udanet_stambecco_llama-3.1-8b_lora with Docker Model Runner:
docker model run hf.co/udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
How to use udanet/udanet_stambecco_llama-3.1-8b_lora with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull udanet/udanet_stambecco_llama-3.1-8b_lora:Q8_0
lemonade run user.udanet_stambecco_llama-3.1-8b_lora-Q8_0
lemonade list
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
8-bit