Text Generation
Transformers
PyTorch
English
gpt2
language-model
transformer
tiny-shakespeare
text-generation-inference
Instructions to use Pavloria/mini-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pavloria/mini-language-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pavloria/mini-language-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Pavloria/mini-language-model") model = AutoModel.from_pretrained("Pavloria/mini-language-model") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Pavloria/mini-language-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pavloria/mini-language-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pavloria/mini-language-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Pavloria/mini-language-model
- SGLang
How to use Pavloria/mini-language-model 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 "Pavloria/mini-language-model" \ --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": "Pavloria/mini-language-model", "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 "Pavloria/mini-language-model" \ --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": "Pavloria/mini-language-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Pavloria/mini-language-model with Docker Model Runner:
docker model run hf.co/Pavloria/mini-language-model
Upload folder using huggingface_hub
Browse files- config.json +1 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +24 -0
- tokenizer_config.json +23 -0
- vocab.json +0 -0
config.json
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{"architectures": ["MiniDecoderModel"]}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:60d73b48c47f4804f5196fb387356a6ed126d5c38eedfe080e57a07e287d5037
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size 56952803
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|endoftext|>",
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"50256": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": {},
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"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>"
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}
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vocab.json
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