Instructions to use balawmt/LanguageModel_Trial_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balawmt/LanguageModel_Trial_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="balawmt/LanguageModel_Trial_1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("balawmt/LanguageModel_Trial_1") model = AutoModelForCausalLM.from_pretrained("balawmt/LanguageModel_Trial_1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use balawmt/LanguageModel_Trial_1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "balawmt/LanguageModel_Trial_1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "balawmt/LanguageModel_Trial_1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/balawmt/LanguageModel_Trial_1
- SGLang
How to use balawmt/LanguageModel_Trial_1 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 "balawmt/LanguageModel_Trial_1" \ --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": "balawmt/LanguageModel_Trial_1", "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 "balawmt/LanguageModel_Trial_1" \ --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": "balawmt/LanguageModel_Trial_1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use balawmt/LanguageModel_Trial_1 with Docker Model Runner:
docker model run hf.co/balawmt/LanguageModel_Trial_1
LanguageModel_Trial_1 / Checkpoints_1_6_M /gpt2-python-language-model /checkpoint-5130 /scheduler.pt
- Xet hash:
- 8b3d9cd0dd740730a5ada4e73e3e01c35c80f5f5ab09ee7c0eb7a01babcf75ec
- Size of remote file:
- 623 Bytes
- SHA256:
- 889349d3cbf5b5010c0ba7ca85c6a7d913eaedb8ba23c23ddcddc68d1d0ed5bd
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