Instructions to use berwart/TextGeneration1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use berwart/TextGeneration1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="berwart/TextGeneration1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("berwart/TextGeneration1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use berwart/TextGeneration1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "berwart/TextGeneration1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "berwart/TextGeneration1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/berwart/TextGeneration1
- SGLang
How to use berwart/TextGeneration1 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 "berwart/TextGeneration1" \ --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": "berwart/TextGeneration1", "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 "berwart/TextGeneration1" \ --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": "berwart/TextGeneration1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use berwart/TextGeneration1 with Docker Model Runner:
docker model run hf.co/berwart/TextGeneration1
Create inference.py
Browse files- inference.py +21 -0
inference.py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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class HelloWorldModel(AutoModelForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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def forward(self, input_ids, **kwargs):
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return {"logits": input_ids}
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def generate_text(prompt):
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model = HelloWorldModel.from_pretrained(".")
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tokenizer = AutoTokenizer.from_pretrained(".")
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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if __name__ == "__main__":
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prompt = "hello"
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print(generate_text(prompt))
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