Instructions to use Tanhim/gpt2-model-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanhim/gpt2-model-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tanhim/gpt2-model-de")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Tanhim/gpt2-model-de") model = AutoModelForCausalLM.from_pretrained("Tanhim/gpt2-model-de") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Tanhim/gpt2-model-de with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tanhim/gpt2-model-de" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tanhim/gpt2-model-de", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Tanhim/gpt2-model-de
- SGLang
How to use Tanhim/gpt2-model-de 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 "Tanhim/gpt2-model-de" \ --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": "Tanhim/gpt2-model-de", "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 "Tanhim/gpt2-model-de" \ --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": "Tanhim/gpt2-model-de", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Tanhim/gpt2-model-de with Docker Model Runner:
docker model run hf.co/Tanhim/gpt2-model-de
Update
Browse files- config.json +37 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
config.json
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{
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"_name_or_path": "anonymous-german-nlp/german-gpt2",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"resid_pdrop": 0.1,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"transformers_version": "4.6.1",
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"use_cache": true,
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"vocab_size": 52000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2f9fd636bc1a6943f1854e92385102c15f91ab40a01a01dfa519c7671a3fb792
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size 515758313
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aeb50566ac9ffea9e7318da4b88b94effd2d7d76d9267c36bb3f616391f369c
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size 2415
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