Instructions to use Arjun-G-Ravi/GPT2-Alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arjun-G-Ravi/GPT2-Alpaca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arjun-G-Ravi/GPT2-Alpaca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Arjun-G-Ravi/GPT2-Alpaca") model = AutoModelForCausalLM.from_pretrained("Arjun-G-Ravi/GPT2-Alpaca") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Arjun-G-Ravi/GPT2-Alpaca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arjun-G-Ravi/GPT2-Alpaca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arjun-G-Ravi/GPT2-Alpaca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arjun-G-Ravi/GPT2-Alpaca
- SGLang
How to use Arjun-G-Ravi/GPT2-Alpaca 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 "Arjun-G-Ravi/GPT2-Alpaca" \ --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": "Arjun-G-Ravi/GPT2-Alpaca", "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 "Arjun-G-Ravi/GPT2-Alpaca" \ --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": "Arjun-G-Ravi/GPT2-Alpaca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arjun-G-Ravi/GPT2-Alpaca with Docker Model Runner:
docker model run hf.co/Arjun-G-Ravi/GPT2-Alpaca
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library_name: transformers
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pipeline_tag: text-generation
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This is the fine tuned version of OpenAI's GPT-2 with Alpaca dataset.
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As of my tests, the best resuts were obtained with:
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But free feel to test different values for the same.
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```
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"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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Instruction:{question}
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Replace {question} with the question of your choice.
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# Model Examples
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Stay hydrated and active’ while exercising. ’ Get enough sleep, exercise, and have a balanced meal plan. ’Eat healthy and energy-packed snacks to stay active and healthy. ’Find the perfect combination of foods for your body, mind, and body. ’Preventative exercise can help you lose weight and improve metabolism. ’ Get enough sleep and stretch during the day. ’Eat healthy and have a balanced and energizing breakfast!
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```
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library_name: transformers
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pipeline_tag: text-generation
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# Model Card for Model ID
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This is the fine tuned version of OpenAI's GPT-2 with Alpaca dataset.
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This has been fine tuned for 20 epochs at batch size of 14.
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### Model Description
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license: mit
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dataset: tatsu-lab/alpaca
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language: en
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library_name: transformers
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pipeline_tag: text-generation
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base_model: gpt2
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```
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## Examples
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## Bias, Risks, and Limitations
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This model has all the biases, risks and limitations of base gpt2 model.
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## Recommendation
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The ideal format for inference is:
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```
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"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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Instruction:{question}
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Replace {question} with the question of your choice.
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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