Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use agaresd/tiny_gpt_deepL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agaresd/tiny_gpt_deepL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="agaresd/tiny_gpt_deepL")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("agaresd/tiny_gpt_deepL") model = AutoModelForCausalLM.from_pretrained("agaresd/tiny_gpt_deepL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use agaresd/tiny_gpt_deepL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "agaresd/tiny_gpt_deepL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agaresd/tiny_gpt_deepL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/agaresd/tiny_gpt_deepL
- SGLang
How to use agaresd/tiny_gpt_deepL 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 "agaresd/tiny_gpt_deepL" \ --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": "agaresd/tiny_gpt_deepL", "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 "agaresd/tiny_gpt_deepL" \ --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": "agaresd/tiny_gpt_deepL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use agaresd/tiny_gpt_deepL with Docker Model Runner:
docker model run hf.co/agaresd/tiny_gpt_deepL
tiny_gpt_deepL
Browse files
README.md
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This model is a fine-tuned version of [sshleifer/tiny-gpt2](https://huggingface.co/sshleifer/tiny-gpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 180 | 10.
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### Framework versions
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This model is a fine-tuned version of [sshleifer/tiny-gpt2](https://huggingface.co/sshleifer/tiny-gpt2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 10.7953
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 180 | 10.8017 |
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| No log | 2.0 | 360 | 10.7968 |
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| 10.7996 | 3.0 | 540 | 10.7953 |
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### Framework versions
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model.safetensors
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