Text Generation
Transformers
PyTorch
English
bart
text2text-generation
rag
question answering
retrieval augmented generation
Instructions to use ansukla/task-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ansukla/task-llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ansukla/task-llm")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ansukla/task-llm") model = AutoModelForSeq2SeqLM.from_pretrained("ansukla/task-llm") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ansukla/task-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ansukla/task-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ansukla/task-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ansukla/task-llm
- SGLang
How to use ansukla/task-llm 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 "ansukla/task-llm" \ --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": "ansukla/task-llm", "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 "ansukla/task-llm" \ --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": "ansukla/task-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ansukla/task-llm with Docker Model Runner:
docker model run hf.co/ansukla/task-llm
Upload BartForConditionalGeneration
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"_name_or_path": "/nlmatics/training/models/bart-summarizer/
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"activation_dropout": 0.1,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"_name_or_path": "/nlmatics/training/models/bart-summarizer/after_macronlg_195000",
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"activation_dropout": 0.1,
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"activation_function": "gelu",
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"add_bias_logits": false,
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