How to use SveBorg/gemmpen-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SveBorg/gemmpen-lora") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SveBorg/gemmpen-lora", dtype="auto")
How to use SveBorg/gemmpen-lora with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SveBorg/gemmpen-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SveBorg/gemmpen-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/SveBorg/gemmpen-lora
How to use SveBorg/gemmpen-lora with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "SveBorg/gemmpen-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SveBorg/gemmpen-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "SveBorg/gemmpen-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SveBorg/gemmpen-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use SveBorg/gemmpen-lora with Unsloth Studio:
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 SveBorg/gemmpen-lora to start chatting
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 SveBorg/gemmpen-lora to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SveBorg/gemmpen-lora to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SveBorg/gemmpen-lora", max_seq_length=2048, )
How to use SveBorg/gemmpen-lora with Docker Model Runner:
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