Instructions to use Ashish9879/aic_shakespeare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ashish9879/aic_shakespeare with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ashish9879/aic_shakespeare")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ashish9879/aic_shakespeare") model = AutoModelForCausalLM.from_pretrained("Ashish9879/aic_shakespeare") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ashish9879/aic_shakespeare with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ashish9879/aic_shakespeare" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ashish9879/aic_shakespeare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ashish9879/aic_shakespeare
- SGLang
How to use Ashish9879/aic_shakespeare 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 "Ashish9879/aic_shakespeare" \ --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": "Ashish9879/aic_shakespeare", "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 "Ashish9879/aic_shakespeare" \ --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": "Ashish9879/aic_shakespeare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ashish9879/aic_shakespeare with Docker Model Runner:
docker model run hf.co/Ashish9879/aic_shakespeare
Commit ·
1b1e702
1
Parent(s): 7ba2a97
Upload config.json with huggingface_hub
Browse files- config.json +74 -0
config.json
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{
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"_name_or_path": "EleutherAI/gpt-neo-1.3B",
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"activation_function": "gelu_new",
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"architectures": [
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"GPTNeoForCausalLM"
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],
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"attention_dropout": 0,
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"attention_layers": [
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"global",
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"local",
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],
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"attention_types": [
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[
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[
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"global",
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"local"
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],
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12
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]
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],
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"bos_token_id": 50256,
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"embed_dropout": 0,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": null,
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"layer_norm_epsilon": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neo",
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"num_heads": 16,
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"num_layers": 24,
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"resid_dropout": 0,
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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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"temperature": 0.9
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}
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},
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"tokenizer_class": "GPT2Tokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.27.4",
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"use_cache": true,
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"vocab_size": 50257,
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"window_size": 256
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}
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