Instructions to use YMDChain/Aide-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YMDChain/Aide-AI with Transformers:

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

pipe = pipeline("text-generation", model="YMDChain/Aide-AI")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YMDChain/Aide-AI")
model = AutoModelForCausalLM.from_pretrained("YMDChain/Aide-AI", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YMDChain/Aide-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YMDChain/Aide-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YMDChain/Aide-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YMDChain/Aide-AI

SGLang

How to use YMDChain/Aide-AI 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 "YMDChain/Aide-AI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YMDChain/Aide-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "YMDChain/Aide-AI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YMDChain/Aide-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YMDChain/Aide-AI with Docker Model Runner:
```
docker model run hf.co/YMDChain/Aide-AI
```

Aide-AI

Model Description

Aide-AI is a fine-tuned GPT-2 model designed for generating conversational text. It is based on the gpt2 base model and has been trained on a small dataset of conversational exchanges.

Usage

Load and use the model with the Transformers library:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("YMDChain/Aide-AI")
tokenizer = GPT2Tokenizer.from_pretrained("YMDChain/Aide-AI")
tokenizer.pad_token = tokenizer.eos_token

input_text = "Hello, how are you?"
encodings = tokenizer(input_text, return_tensors="pt")
output = model.generate(**encodings, max_length=50, do_sample=True, top_p=0.95)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Downloads last month: 19

Safetensors

Model size

0.1B params

Tensor type

F32