Text Generation
Transformers
PyTorch
Safetensors
English
gpt_neox
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use postbot/pythia-160m-hq-emails with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use postbot/pythia-160m-hq-emails with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="postbot/pythia-160m-hq-emails")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("postbot/pythia-160m-hq-emails") model = AutoModelForCausalLM.from_pretrained("postbot/pythia-160m-hq-emails", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use postbot/pythia-160m-hq-emails with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "postbot/pythia-160m-hq-emails" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "postbot/pythia-160m-hq-emails", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/postbot/pythia-160m-hq-emails
- SGLang
How to use postbot/pythia-160m-hq-emails with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "postbot/pythia-160m-hq-emails" \ --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": "postbot/pythia-160m-hq-emails", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
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 "postbot/pythia-160m-hq-emails" \ --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": "postbot/pythia-160m-hq-emails", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use postbot/pythia-160m-hq-emails with Docker Model Runner:
docker model run hf.co/postbot/pythia-160m-hq-emails
Download training_args.bin from postbot/pythia-160m-hq-emails: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/postbot/pythia-160m-hq-emails/resolve/main/training_args.bin
- Command line
-
hf download hf://postbot/pythia-160m-hq-emails/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/postbot/pythia-160m-hq-emails/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- d26f0a139937a791f78ffdb934c995cfcf7ef468dac2cac1814c32a8e23c4e2f
- Size of remote file:
- 3.58 kB
- SHA256:
- 37cde2ab0d710a1c5c2dda82b76cce06844947579560f0f629d8ab8a34d8c33e
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