Instructions to use Triangles/fantastic_philosopher_124_4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Triangles/fantastic_philosopher_124_4000 with Transformers:

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

pipe = pipeline("text-generation", model="Triangles/fantastic_philosopher_124_4000")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Triangles/fantastic_philosopher_124_4000")
model = AutoModelForMultimodalLM.from_pretrained("Triangles/fantastic_philosopher_124_4000")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Triangles/fantastic_philosopher_124_4000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Triangles/fantastic_philosopher_124_4000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Triangles/fantastic_philosopher_124_4000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Triangles/fantastic_philosopher_124_4000

SGLang

How to use Triangles/fantastic_philosopher_124_4000 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 "Triangles/fantastic_philosopher_124_4000" \
    --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": "Triangles/fantastic_philosopher_124_4000",
		"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 "Triangles/fantastic_philosopher_124_4000" \
        --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": "Triangles/fantastic_philosopher_124_4000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Triangles/fantastic_philosopher_124_4000 with Docker Model Runner:
```
docker model run hf.co/Triangles/fantastic_philosopher_124_4000
```

Model Description

finetuned using aitextgen on a dataset including philosophy books, r/showerthoughts, and original texts generated by chatGPT. Please report any offensive content, etc!

Developed by: Colleen Macklin
Model type: GPT Neo Small (125m)
Language(s) (NLP): en
License: [More Information Needed]
Finetuned from model [optional]: GPT Neo Small (125m)

Uses

For creative projects and dialogue generation.

Bias, Risks, and Limitations

This model generates unsupervised random output has the potential to offend with biased outputs. Please use responsibly.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

license: apache-2.0 language: - en co2_eq_emissions: emissions: .05kg Co2 eq. source: "https://mlco2.github.io/impact" training_type: "fine-tuning" geographical_location: "Brooklyn, NY, USA. To check your compute's electricity grid, you can check out https://app.electricitymap.org." hardware_used: "T4 (Google Colab)"

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Paper for Triangles/fantastic_philosopher_124_4000

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 51