Instructions to use Crataco/Pythia-160M-Deduped-Adventure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crataco/Pythia-160M-Deduped-Adventure with Transformers:

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

pipe = pipeline("text-generation", model="Crataco/Pythia-160M-Deduped-Adventure")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Crataco/Pythia-160M-Deduped-Adventure")
model = AutoModelForCausalLM.from_pretrained("Crataco/Pythia-160M-Deduped-Adventure")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Crataco/Pythia-160M-Deduped-Adventure with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Crataco/Pythia-160M-Deduped-Adventure"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crataco/Pythia-160M-Deduped-Adventure",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Crataco/Pythia-160M-Deduped-Adventure

SGLang

How to use Crataco/Pythia-160M-Deduped-Adventure 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 "Crataco/Pythia-160M-Deduped-Adventure" \
    --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": "Crataco/Pythia-160M-Deduped-Adventure",
		"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 "Crataco/Pythia-160M-Deduped-Adventure" \
        --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": "Crataco/Pythia-160M-Deduped-Adventure",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Crataco/Pythia-160M-Deduped-Adventure with Docker Model Runner:
```
docker model run hf.co/Crataco/Pythia-160M-Deduped-Adventure
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

pythia-160m-deduped-aid

Model description

This model is a finetune of EleutherAI/pythia-160m-deduped (from when it was instead pythia-125m-deduped), on the text_adventures.txt dataset originally intended for AI Dungeon 2. Performance will be very poor, as expected by the small model, and generations may be offensive thanks to its training data.

This model was trained for testing purposes as the successor to Merry/AID-Neo-125M and was intended for use with KoboldAI. A temperature of 0.5 and a repetition penalty of 1.05 were tested.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Crataco/Pythia-160M-Deduped-Adventure

Quantizations

2 models