Instructions to use BigSalmon/Infill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/Infill with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/Infill")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill") model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BigSalmon/Infill with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/Infill" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/Infill", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/Infill
- SGLang
How to use BigSalmon/Infill 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 "BigSalmon/Infill" \ --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": "BigSalmon/Infill", "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 "BigSalmon/Infill" \ --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": "BigSalmon/Infill", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/Infill with Docker Model Runner:
docker model run hf.co/BigSalmon/Infill
Update README.md
Browse files
README.md
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@@ -12,7 +12,7 @@ https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
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```
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```
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prompt = """
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input_ids = tokenizer.encode(prompt, return_tensors='pt')
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outputs = model.generate(input_ids=input_ids,
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max_length=10 + len(prompt),
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```
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Most likely outputs (Disclaimer: I highly recommend using this over just generating):
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```
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prompt = """
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text = tokenizer.encode(prompt)
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myinput, past_key_values = torch.tensor([text]), None
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myinput = myinput
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```
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```
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prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
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input_ids = tokenizer.encode(prompt, return_tensors='pt')
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outputs = model.generate(input_ids=input_ids,
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max_length=10 + len(prompt),
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```
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Most likely outputs (Disclaimer: I highly recommend using this over just generating):
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```
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prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
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text = tokenizer.encode(prompt)
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myinput, past_key_values = torch.tensor([text]), None
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myinput = myinput
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