Instructions to use pymlex/ipa-transcriptor-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pymlex/ipa-transcriptor-300M with Transformers:

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

pipe = pipeline("text-generation", model="pymlex/ipa-transcriptor-300M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("pymlex/ipa-transcriptor-300M")
model = AutoModelForSeq2SeqLM.from_pretrained("pymlex/ipa-transcriptor-300M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pymlex/ipa-transcriptor-300M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pymlex/ipa-transcriptor-300M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pymlex/ipa-transcriptor-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pymlex/ipa-transcriptor-300M

SGLang

How to use pymlex/ipa-transcriptor-300M 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 "pymlex/ipa-transcriptor-300M" \
    --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": "pymlex/ipa-transcriptor-300M",
		"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 "pymlex/ipa-transcriptor-300M" \
        --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": "pymlex/ipa-transcriptor-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pymlex/ipa-transcriptor-300M with Docker Model Runner:
```
docker model run hf.co/pymlex/ipa-transcriptor-300M
```

ipa-transcriptor-300M / README.md

Commit History

Sep 5

10:50
Adding ONNX file of this model⁠

realenv

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e20cd62

May 29

05:04
Update README.md

pymlex

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May 24

16:55
Fix pipeline tag in model card

pymlex

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9dfc71e
16:55
Update model card with metrics tables and training plots

pymlex

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16:18
Upload T5ForConditionalGeneration

pymlex

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Commit History

Sep 5

Adding ONNX file of this model⁠

May 29

Update README.md

May 24

Fix pipeline tag in model card

Update model card with metrics tables and training plots

Upload T5ForConditionalGeneration