Instructions to use VidaEdco/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VidaEdco/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VidaEdco/results")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("VidaEdco/results") model = AutoModelForCausalLM.from_pretrained("VidaEdco/results") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use VidaEdco/results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VidaEdco/results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VidaEdco/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VidaEdco/results
- SGLang
How to use VidaEdco/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 "VidaEdco/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": "VidaEdco/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 "VidaEdco/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": "VidaEdco/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VidaEdco/results with Docker Model Runner:
docker model run hf.co/VidaEdco/results
Model save
Browse files- README.md +3 -3
- generation_config.json +3 -2
README.md
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library_name: transformers
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- generated_from_trainer
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model-index:
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# results
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This model is a fine-tuned version of [
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## Model description
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---
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library_name: transformers
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license: other
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base_model: facebook/opt-350m
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tags:
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- generated_from_trainer
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# results
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
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## Model description
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generation_config.json
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"_from_model_config": true,
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"transformers_version": "4.44.2"
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"pad_token_id": 1,
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"transformers_version": "4.44.2"
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}
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