Instructions to use shivam001/deibotquestion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivam001/deibotquestion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shivam001/deibotquestion", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("shivam001/deibotquestion", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shivam001/deibotquestion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shivam001/deibotquestion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shivam001/deibotquestion", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shivam001/deibotquestion
- SGLang
How to use shivam001/deibotquestion 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 "shivam001/deibotquestion" \ --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": "shivam001/deibotquestion", "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 "shivam001/deibotquestion" \ --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": "shivam001/deibotquestion", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shivam001/deibotquestion with Docker Model Runner:
docker model run hf.co/shivam001/deibotquestion
Commit History
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Create handler.py
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Create requirements.txt
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Rename modeling_RW.py to modelling_RW.py
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Create modeling_RW.py
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Create configuration_RW.py
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Update config.json
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Upload tokenizer (#3)
- Upload tokenizer (0171378590c40a7762f8287087be1ea1d57d2eca)350abaa -
Upload RWForCausalLM (#2)
- Upload RWForCausalLM (637414a7693f1104eb588ce4a0585e28b1c85aa0)691148e -
Update adapter_config.json
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Rename config.json to adapter_config.json
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Rename adapter_config.json to config.json
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Update adapter_config.json
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Upload model (#1)
- Upload model (dc9bd3cf3d584e004fe2b2f1dc6e938c33f1263c)cce258d -
initial commit
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