Text Generation
Transformers
PyTorch
Safetensors
gpt_neo
Generated from Trainer
text generation
email generation
email
Eval Results (legacy)
Instructions to use postbot/gpt-neo-1.3B-emailgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use postbot/gpt-neo-1.3B-emailgen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="postbot/gpt-neo-1.3B-emailgen")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("postbot/gpt-neo-1.3B-emailgen") model = AutoModelForCausalLM.from_pretrained("postbot/gpt-neo-1.3B-emailgen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use postbot/gpt-neo-1.3B-emailgen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "postbot/gpt-neo-1.3B-emailgen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "postbot/gpt-neo-1.3B-emailgen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/postbot/gpt-neo-1.3B-emailgen
- SGLang
How to use postbot/gpt-neo-1.3B-emailgen 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/gpt-neo-1.3B-emailgen" \ --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/gpt-neo-1.3B-emailgen", "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/gpt-neo-1.3B-emailgen" \ --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/gpt-neo-1.3B-emailgen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use postbot/gpt-neo-1.3B-emailgen with Docker Model Runner:
docker model run hf.co/postbot/gpt-neo-1.3B-emailgen
Download pytorch_model.bin from postbot/gpt-neo-1.3B-emailgen: direct link, hf CLI and curl.
- Browser
- Download file 5.36 GB
-
https://huggingface.co/postbot/gpt-neo-1.3B-emailgen/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://postbot/gpt-neo-1.3B-emailgen/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/postbot/gpt-neo-1.3B-emailgen/resolve/main/pytorch_model.bin
5.36 GB
- Xet hash:
- ca24607ffab2f09309bdbc07190034e2011120873bbbc2087003aaf11008c1ea
- Size of remote file:
- 5.36 GB
- SHA256:
- 08be47da43718499b006a4aff7f17a3e1174555f954555598a1e77fef80b34ad
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