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
```

luel commited on Nov 8, 2024

Commit

3bd863e

verified ·

1 Parent(s): 2ae3391

minor change

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ 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
@@ -23,9 +23,9 @@ This is a GPT-2 model trained from scratch on Tigrinya text data. It was trained
 - 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

 # 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.
 #### Model Description
 - Model type: GPT-2
 - Finetuned from model: Trained from scratch (no pre-training)
 #### Model Architecture
+- Parameters: 51.9M
 - Context Window: 128 tokens
+- Vocabulary Size: 52,000
 #### Training Details
 - Training regime: fp16 mixed precision