Instructions to use Team-Aiko/TeamAiko-GPT-Neo-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Team-Aiko/TeamAiko-GPT-Neo-1.3B with Transformers:

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

pipe = pipeline("text-generation", model="Team-Aiko/TeamAiko-GPT-Neo-1.3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Team-Aiko/TeamAiko-GPT-Neo-1.3B")
model = AutoModelForCausalLM.from_pretrained("Team-Aiko/TeamAiko-GPT-Neo-1.3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Team-Aiko/TeamAiko-GPT-Neo-1.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Team-Aiko/TeamAiko-GPT-Neo-1.3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Team-Aiko/TeamAiko-GPT-Neo-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Team-Aiko/TeamAiko-GPT-Neo-1.3B

SGLang

How to use Team-Aiko/TeamAiko-GPT-Neo-1.3B 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 "Team-Aiko/TeamAiko-GPT-Neo-1.3B" \
    --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": "Team-Aiko/TeamAiko-GPT-Neo-1.3B",
		"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 "Team-Aiko/TeamAiko-GPT-Neo-1.3B" \
        --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": "Team-Aiko/TeamAiko-GPT-Neo-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Team-Aiko/TeamAiko-GPT-Neo-1.3B with Docker Model Runner:
```
docker model run hf.co/Team-Aiko/TeamAiko-GPT-Neo-1.3B
```

TeamAiko-GPT-Neo-1.3B

This is the TeamAiko-GPT-Neo-1.3B model, a customized version of the EleutherAI/gpt-neo-1.3B model. This model has been branded and configured for use by Team Aiko. Note: This is the base version of the model and has not been trained on any specific datasets.

Model Details

Model Name: TeamAiko-GPT-Neo-1.3B
Base Model: EleutherAI/gpt-neo-1.3B
Architecture: GPT-Neo
Parameters: 1.3 billion
Tokenizer: AutoTokenizer
Framework: PyTorch

Usage

To use this model, you can load it with the transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Path to the model folder
model_path = "Team-Aiko/TeamAiko-GPT-Neo-1.3B"

# Set device to CPU to limit RAM usage
device = torch.device("cpu")
torch.set_num_threads(4)  # Limit the number of threads used by PyTorch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)  # Move model to CPU

# Test the model with a sample input
input_text = "Once upon a time in a land far, far away"
inputs = tokenizer(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated text:", generated_text)

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