Instructions to use realsanjeev/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use realsanjeev/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="realsanjeev/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("realsanjeev/results") model = AutoModelForSeq2SeqLM.from_pretrained("realsanjeev/results") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use realsanjeev/results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "realsanjeev/results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "realsanjeev/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/realsanjeev/results
- SGLang
How to use realsanjeev/results 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 "realsanjeev/results" \ --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": "realsanjeev/results", "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 "realsanjeev/results" \ --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": "realsanjeev/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use realsanjeev/results with Docker Model Runner:
docker model run hf.co/realsanjeev/results
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by sanjeev-bhandari - opened
README.md
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model-index:
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- name: results
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# results
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This model is a fine-tuned version of [pascalrai/nep-summ-BART](https://huggingface.co/pascalrai/nep-summ-BART) on an unknown dataset.
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## Model description
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Tokenizers 0.15.2
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model-index:
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- name: results
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results: []
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datasets:
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- sanjeev-bhandari01/nepali-summarization-dataset
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- sanjeev-bhandari01/XLSum-nepali-summerization-dataset
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language:
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- ne
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metrics:
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- rouge
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pipeline_tag: summarization
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# results
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<!--This model is a fine-tuned version of [pascalrai/nep-summ-BART](https://huggingface.co/pascalrai/nep-summ-BART) on an unknown dataset.-->
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## Model description
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Tokenizers 0.15.2
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