Instructions to use nguyenvulebinh/deltalm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nguyenvulebinh/deltalm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nguyenvulebinh/deltalm-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("nguyenvulebinh/deltalm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nguyenvulebinh/deltalm-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nguyenvulebinh/deltalm-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nguyenvulebinh/deltalm-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nguyenvulebinh/deltalm-base
- SGLang
How to use nguyenvulebinh/deltalm-base 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 "nguyenvulebinh/deltalm-base" \ --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": "nguyenvulebinh/deltalm-base", "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 "nguyenvulebinh/deltalm-base" \ --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": "nguyenvulebinh/deltalm-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nguyenvulebinh/deltalm-base with Docker Model Runner:
docker model run hf.co/nguyenvulebinh/deltalm-base
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```python from modeling_deltalm import DeltalmForConditionalGeneration # modeling_deltalm: https://huggingface.co/nguyenvulebinh/deltalm-base/blob/main/modeling_deltalm.py from configuration_deltalm import DeltalmConfig # configuration_deltalm: https://huggingface.co/nguyenvulebinh/deltalm-base/blob/main/configuration_deltalm.py from transformers AutoTokenizer src_text = "i'm steve and<mask> 25 years old" encoded_hi = tokenizer(src_text, return_tensors="pt") generated_output = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.bos_token_id, max_length=20, num_beams=1, return_dict_in_generate=True, return_dict=True, output_hidden_states=True) text_output = tokenizer.batch_decode(generated_output.sequences, skip_special_tokens=True) print(text_output) ```60b68bc -
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