Instructions to use Harley-ml/Dillion-1.2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harley-ml/Dillion-1.2M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Harley-ml/Dillion-1.2M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Harley-ml/Dillion-1.2M") model = AutoModelForCausalLM.from_pretrained("Harley-ml/Dillion-1.2M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Harley-ml/Dillion-1.2M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Harley-ml/Dillion-1.2M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harley-ml/Dillion-1.2M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Harley-ml/Dillion-1.2M
- SGLang
How to use Harley-ml/Dillion-1.2M 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 "Harley-ml/Dillion-1.2M" \ --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": "Harley-ml/Dillion-1.2M", "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 "Harley-ml/Dillion-1.2M" \ --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": "Harley-ml/Dillion-1.2M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Harley-ml/Dillion-1.2M with Docker Model Runner:
docker model run hf.co/Harley-ml/Dillion-1.2M
Update README.md
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README.md
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@@ -110,3 +110,12 @@ We trained Dillion for 0.71 epochs on 14B (only saw ~9B) tokens of FineWeb-edu o
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| SWAG | acc_norm | 0.3036 | — | 0.3297 |
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| WikiText | bits_per_byte | 1.6161 | — | — |
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| WikiText | byte_perplexity | 3.0655 | 3.1652 | — |
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| SWAG | acc_norm | 0.3036 | — | 0.3297 |
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| WikiText | bits_per_byte | 1.6161 | — | — |
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| WikiText | byte_perplexity | 3.0655 | 3.1652 | — |
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## Generation Examples
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Prompt: `The`
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Output:
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```
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Twitter and Freees of Brooklyn Press, Oxford University.
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The Home Council of the Monthly Landing Foundation is a partner with the Great War in the South. The Eighteenth Century has been held on the River Battalion by the Vietnam War, which was laid down by the German Empire to the Nazis. Its first-year period was born on May 1, 1846.
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```
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