Instructions to use migueldeguzmandev/GPT2XL-RLLM-11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use migueldeguzmandev/GPT2XL-RLLM-11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="migueldeguzmandev/GPT2XL-RLLM-11")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("migueldeguzmandev/GPT2XL-RLLM-11") model = AutoModelForCausalLM.from_pretrained("migueldeguzmandev/GPT2XL-RLLM-11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use migueldeguzmandev/GPT2XL-RLLM-11 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "migueldeguzmandev/GPT2XL-RLLM-11" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "migueldeguzmandev/GPT2XL-RLLM-11", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/migueldeguzmandev/GPT2XL-RLLM-11
- SGLang
How to use migueldeguzmandev/GPT2XL-RLLM-11 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 "migueldeguzmandev/GPT2XL-RLLM-11" \ --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": "migueldeguzmandev/GPT2XL-RLLM-11", "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 "migueldeguzmandev/GPT2XL-RLLM-11" \ --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": "migueldeguzmandev/GPT2XL-RLLM-11", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use migueldeguzmandev/GPT2XL-RLLM-11 with Docker Model Runner:
docker model run hf.co/migueldeguzmandev/GPT2XL-RLLM-11
Download pytorch_model.bin from migueldeguzmandev/GPT2XL-RLLM-11: direct link, hf CLI and curl.
- Browser
- Download file 6.23 GB
-
https://huggingface.co/migueldeguzmandev/GPT2XL-RLLM-11/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://migueldeguzmandev/GPT2XL-RLLM-11/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/migueldeguzmandev/GPT2XL-RLLM-11/resolve/main/pytorch_model.bin
6.23 GB
- Xet hash:
- a76e95a3199d7807296fb134c07ebc5dfe7d85fdddc0ebd41254ab768e48a180
- Size of remote file:
- 6.23 GB
- SHA256:
- 3fce88ec9365e8586ed489c726dbac8f2596fa823db0a36878e713c2157697a5
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