Instructions to use AbidHasan95/smsner_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbidHasan95/smsner_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AbidHasan95/smsner_model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbidHasan95/smsner_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AbidHasan95/smsner_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AbidHasan95/smsner_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbidHasan95/smsner_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AbidHasan95/smsner_model
- SGLang
How to use AbidHasan95/smsner_model 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 "AbidHasan95/smsner_model" \ --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": "AbidHasan95/smsner_model", "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 "AbidHasan95/smsner_model" \ --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": "AbidHasan95/smsner_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AbidHasan95/smsner_model with Docker Model Runner:
docker model run hf.co/AbidHasan95/smsner_model
updated handler.py
Browse files- handler.py +2 -0
handler.py
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from typing import Dict, List, Any
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import torch
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import torch.nn as nn
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from transformers import pipeline, BertModel, AutoTokenizer, PretrainedConfig
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class EndpointHandler():
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# run normal prediction
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prediction = self.model.classify(inputs)
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return prediction
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class CustomModel(nn.Module):
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from typing import Dict, List, Any
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import torch
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import torch.nn as nn
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import json
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from transformers import pipeline, BertModel, AutoTokenizer, PretrainedConfig
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class EndpointHandler():
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# run normal prediction
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prediction = self.model.classify(inputs)
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prediction = json.dumps(prediction)
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return prediction
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class CustomModel(nn.Module):
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