Instructions to use DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt with Transformers:

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

pipe = pipeline("text-generation", model="DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")
model = AutoModelForCausalLM.from_pretrained("DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt

SGLang

How to use DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt 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 "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt" \
    --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": "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt",
		"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 "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt" \
        --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": "DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt with Docker Model Runner:
```
docker model run hf.co/DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt
```

library_name: transformers

Model Card for aristotle_based_on_rugpt3large_based_on_gpt

Model Description

This model is based on the RuGPT-3 Large architecture and fine-tuned on Aristotle's texts. It is designed for generating text in the style of Aristotle.

Intended Use

This model can be used for text generation tasks, particularly for creating philosophical texts or dialogues.

Limitations

The model may reflect biases present in the training data. It is important to evaluate its outputs critically.

How to Use

You can load this model using the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")
model = AutoModelForCausalLM.from_pretrained("DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt")

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Safetensors

Model size

0.8B params

Tensor type

F32

Model tree for DmitryYarov/aristotle_based_on_rugpt3large_based_on_gpt

Base model

ai-forever/rugpt3large_based_on_gpt2

Finetuned

(8)

this model

DmitryYarov
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aristotle_based_on_rugpt3large_based_on_gpt