Instructions to use luel/gpt2-tigrinya-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luel/gpt2-tigrinya-medium with Transformers:

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

pipe = pipeline("text-generation", model="luel/gpt2-tigrinya-medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luel/gpt2-tigrinya-medium")
model = AutoModelForCausalLM.from_pretrained("luel/gpt2-tigrinya-medium")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use luel/gpt2-tigrinya-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luel/gpt2-tigrinya-medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luel/gpt2-tigrinya-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/luel/gpt2-tigrinya-medium

SGLang

How to use luel/gpt2-tigrinya-medium 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 "luel/gpt2-tigrinya-medium" \
    --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": "luel/gpt2-tigrinya-medium",
		"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 "luel/gpt2-tigrinya-medium" \
        --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": "luel/gpt2-tigrinya-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use luel/gpt2-tigrinya-medium with Docker Model Runner:
```
docker model run hf.co/luel/gpt2-tigrinya-medium
```

gpt2-tigrinya-medium / README.md

luel

Update README.md

f76a9d9 verified over 1 year ago

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language: ti
license: mit
library_name: transformers
tags:
  - tigrinya
  - gpt2
  - text-generation
metrics:
  - perplexity
  - loss
pipeline_tag: text-generation

Model Card for GPT-2 Tigrinya Medium

Model Summary

This is a GPT-2 model trained from scratch on Tigrinya text data. It was trained on 20.6 million tokens, primarily from news sources. The model is specifically designed for generating Tigrinya text using the Hugging Face Transformers library.

Model Description

Model type: GPT-2
Language: Tigrinya (ትግርኛ)
Finetuned from model: Trained from scratch (no pre-training)

Model Architecture

Parameters: 42.6M
Context Window: 128 tokens
Vocabulary Size: 52,000

Training Details

Training regime: fp16 mixed precision
Number of Epochs: 12
Batch Size: 6 (with gradient accumulation steps of 8)
Learning Rate: 5e-4

Evaluation

Training Perplexity: 28.6
Training Loss: 3.12

Usage

from transformers import pipeline
# Load the model
generator = pipeline('text-generation', model='luel/gpt2-tigrinya-medium')

prompt = "ክልል ትግራይ"
# Generate text
text = generator(prompt, max_length=100)[0]['generated_text']
print(text)

Limitations

Limited context window of 128 tokens.
Best suited for medium-length Tigrinya text generation.
Outputs should be reviewed for accuracy.