How to use tlc4418/pythia_70m_sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tlc4418/pythia_70m_sft")
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tlc4418/pythia_70m_sft") model = AutoModelForCausalLM.from_pretrained("tlc4418/pythia_70m_sft")
How to use tlc4418/pythia_70m_sft with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tlc4418/pythia_70m_sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tlc4418/pythia_70m_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
docker model run hf.co/tlc4418/pythia_70m_sft
How to use tlc4418/pythia_70m_sft with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tlc4418/pythia_70m_sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tlc4418/pythia_70m_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
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 "tlc4418/pythia_70m_sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tlc4418/pythia_70m_sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
How to use tlc4418/pythia_70m_sft with Docker Model Runner:
70m Pythia model after SFT on the AlpacaFarm dataset 'sft' split.
Model used as a base for reward models in 'Reward Model Ensembles Mitigate Overoptimization'
