Instructions to use 12sciencejnv/FinedGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 12sciencejnv/FinedGPT with Transformers:

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

pipe = pipeline("text-generation", model="12sciencejnv/FinedGPT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("12sciencejnv/FinedGPT")
model = AutoModelForCausalLM.from_pretrained("12sciencejnv/FinedGPT")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 12sciencejnv/FinedGPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "12sciencejnv/FinedGPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "12sciencejnv/FinedGPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/12sciencejnv/FinedGPT

SGLang

How to use 12sciencejnv/FinedGPT 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 "12sciencejnv/FinedGPT" \
    --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": "12sciencejnv/FinedGPT",
		"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 "12sciencejnv/FinedGPT" \
        --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": "12sciencejnv/FinedGPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 12sciencejnv/FinedGPT with Docker Model Runner:
```
docker model run hf.co/12sciencejnv/FinedGPT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ChadGPT

This model is a fine-tuned version of GPT-2 on the minhalvp/islamqa dataset, which consists of Islamic question-answer pairs. It is designed for generating answers to Islamic questions.

License

Apache 2.0

Datasets

The model is fine-tuned on the minhalvp/islamqa dataset from Hugging Face.

Language

English

Metrics

Perplexity: 170.31 (calculated on the validation set of the minhalvp/islamqa dataset)
Training Loss: 2.3 (final training loss after fine-tuning)

Base Model

The model is based on the gpt2 architecture.

Pipeline Tag

text-generation

Library Name

transformers

Eval Results

The model achieved a training loss of approximately 2.3 after 3 epochs of fine-tuning.

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for 12sciencejnv/FinedGPT

Base model

openai-community/gpt2

Finetuned

(2159)

this model

Quantizations

1 model

Dataset used to train 12sciencejnv/FinedGPT

Evaluation results

Perplexity on minhalvp/islamqa
Self-evaluated

170.310

12sciencejnv
/

FinedGPT