Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use psxjp5/mlm_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psxjp5/mlm_new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="psxjp5/mlm_new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("psxjp5/mlm_new") model = AutoModelForCausalLM.from_pretrained("psxjp5/mlm_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use psxjp5/mlm_new with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "psxjp5/mlm_new" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "psxjp5/mlm_new", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/psxjp5/mlm_new
- SGLang
How to use psxjp5/mlm_new 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 "psxjp5/mlm_new" \ --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": "psxjp5/mlm_new", "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 "psxjp5/mlm_new" \ --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": "psxjp5/mlm_new", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use psxjp5/mlm_new with Docker Model Runner:
docker model run hf.co/psxjp5/mlm_new
Download training_args.bin from psxjp5/mlm_new: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/psxjp5/mlm_new/resolve/main/training_args.bin
- Command line
-
hf download hf://psxjp5/mlm_new/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/psxjp5/mlm_new/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- fe9e91dea464a7b07c72f4d45cae526ab27a31111409d59f036ed22ca4b33d33
- Size of remote file:
- 3.96 kB
- SHA256:
- 6f20971b6a6e8f38c7d9f46c5afebd2e0c25b4eed80b5046c9b392c8d37a6cb7
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