Instructions to use AISE-TUDelft/Custom-Activations-GPT-PReLU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AISE-TUDelft/Custom-Activations-GPT-PReLU with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AISE-TUDelft/Custom-Activations-GPT-PReLU")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AISE-TUDelft/Custom-Activations-GPT-PReLU", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AISE-TUDelft/Custom-Activations-GPT-PReLU with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AISE-TUDelft/Custom-Activations-GPT-PReLU"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AISE-TUDelft/Custom-Activations-GPT-PReLU",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AISE-TUDelft/Custom-Activations-GPT-PReLU

SGLang

How to use AISE-TUDelft/Custom-Activations-GPT-PReLU 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 "AISE-TUDelft/Custom-Activations-GPT-PReLU" \
    --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": "AISE-TUDelft/Custom-Activations-GPT-PReLU",
		"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 "AISE-TUDelft/Custom-Activations-GPT-PReLU" \
        --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": "AISE-TUDelft/Custom-Activations-GPT-PReLU",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AISE-TUDelft/Custom-Activations-GPT-PReLU with Docker Model Runner:
```
docker model run hf.co/AISE-TUDelft/Custom-Activations-GPT-PReLU
```

Custom-Activations-GPT-PReLU / config.json

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Upload ActivationsGPTNeoForCausalLM

b13f0c5 verified over 2 years ago

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{
  "_name_or_path": "c:\\Users\\Filip\\Documents\\School\\Delft\\Research project\\results\\upload\\GPT-PReLU",
  "activation_function": "gelu_new",
  "architectures": [
    "ActivationsGPTNeoForCausalLM"
  ],
  "attention_dropout": 0.0,
  "attention_layers": [
    "global",
    "local"
  ],
  "attention_types": [
    [
      [
        "global",
        "local"
      ],
      1
    ]
  ],
  "bos_token_id": 50256,
  "classifier_dropout": 0.1,
  "custom_activation": "prelu",
  "embed_dropout": 0.0,
  "eos_token_id": 50256,
  "hidden_size": 512,
  "initializer_range": 0.02,
  "intermediate_size": 1024,
  "layer_norm_epsilon": 1e-05,
  "max_position_embeddings": 512,
  "model_type": "activations_gpt_neo",
  "num_heads": 4,
  "num_layers": 2,
  "pad_token_id": 0,
  "resid_dropout": 0.0,
  "torch_dtype": "float32",
  "transformers_version": "4.26.1",
  "use_cache": true,
  "vocab_size": 10000,
  "window_size": 256
}

	{
	"_name_or_path": "c:\\Users\\Filip\\Documents\\School\\Delft\\Research project\\results\\upload\\GPT-PReLU",
	"activation_function": "gelu_new",
	"architectures": [
	"ActivationsGPTNeoForCausalLM"
	],
	"attention_dropout": 0.0,
	"attention_layers": [
	"global",
	"local"
	],
	"attention_types": [
	[
	[
	"global",
	"local"
	],
	1
	]
	],
	"bos_token_id": 50256,
	"classifier_dropout": 0.1,
	"custom_activation": "prelu",
	"embed_dropout": 0.0,
	"eos_token_id": 50256,
	"hidden_size": 512,
	"initializer_range": 0.02,
	"intermediate_size": 1024,
	"layer_norm_epsilon": 1e-05,
	"max_position_embeddings": 512,
	"model_type": "activations_gpt_neo",
	"num_heads": 4,
	"num_layers": 2,
	"pad_token_id": 0,
	"resid_dropout": 0.0,
	"torch_dtype": "float32",
	"transformers_version": "4.26.1",
	"use_cache": true,
	"vocab_size": 10000,
	"window_size": 256
	}