Instructions to use eliwill/alan-watts-8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eliwill/alan-watts-8e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="eliwill/alan-watts-8e")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("eliwill/alan-watts-8e") model = AutoModelForCausalLM.from_pretrained("eliwill/alan-watts-8e") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use eliwill/alan-watts-8e with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eliwill/alan-watts-8e" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "eliwill/alan-watts-8e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/eliwill/alan-watts-8e
- SGLang
How to use eliwill/alan-watts-8e 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 "eliwill/alan-watts-8e" \ --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": "eliwill/alan-watts-8e", "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 "eliwill/alan-watts-8e" \ --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": "eliwill/alan-watts-8e", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use eliwill/alan-watts-8e with Docker Model Runner:
docker model run hf.co/eliwill/alan-watts-8e
End of training
Browse files
README.md
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss:
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- Validation Loss: 3.
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- Epoch:
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## Model description
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| Train Loss | Validation Loss | Epoch |
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| 3.3770 | 3.5178 | 0 |
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### Framework versions
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 2.9844
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- Validation Loss: 3.3608
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- Epoch: 7
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## Model description
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| Train Loss | Validation Loss | Epoch |
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| 3.3770 | 3.5178 | 0 |
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| 3.2458 | 3.4955 | 1 |
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| 3.1798 | 3.4681 | 2 |
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| 3.1305 | 3.4412 | 3 |
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| 3.0881 | 3.4115 | 4 |
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| 3.0500 | 3.3940 | 5 |
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| 3.0153 | 3.3692 | 6 |
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| 2.9844 | 3.3608 | 7 |
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### Framework versions
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logs/train/events.out.tfevents.1665986796.f9a3fc799c36.138.0.v2
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