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")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("pymlex/ipa-transcriptor-300M")
model = AutoModelForMultimodalLM.from_pretrained("pymlex/ipa-transcriptor-300M")

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

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	---
	license: gpl-3.0
	library_name: transformers
	tags:
	- phonetics
	- ipa
	- byt5
	- seq2seq
	pipeline_tag: text-generation
	base_model: google/byt5-small
	language:
	- en
	metrics:
	- perplexity
	- bleu
	---

	# IPA Transcriptor 300M

	Fine-tuned [`google/byt5-small`](https://huggingface.co/google/byt5-small) for English word to IPA transcription.

	Task format:

	```text
	ipa: analytical -> ˌænəˈlɪtɪkəl
	```

	Training data: [English phonetic and syllable count dictionary](https://www.kaggle.com/datasets/schwartstack/english-phonetic-and-syllable-count-dictionary), 125,925 word–IPA pairs after cleaning.

	GitHub: [pymlex/ipa-transcriptor-300M](https://github.com/pymlex/ipa-transcriptor-300M)

	## Benchmark

	Fine-tuned on NVIDIA L4, run `colab_l4_bf16`, beam search `num_beams=4`.

	\| Metric \| Validation \| Test \|
	\| ------ \| ----------: \| ---: \|
	\| `n_samples` \| 6296 \| 6297 \|
	\| `loss` \| 0.1515 \| 0.1478 \|
	\| `perplexity` \| 1.1636 \| 1.1592 \|
	\| `token_accuracy` \| 0.7849 \| 0.7858 \|
	\| `exact_match` \| 0.5982 \| 0.6111 \|
	\| `char_accuracy` \| 0.8948 \| 0.8969 \|
	\| `cer` \| 0.1066 \| 0.1045 \|
	\| `bleu` \| 59.82 \| 61.11 \|

	## Dataset length distributions

	![Word length](docs/word_length_hist.png)

	![IPA length](docs/ipa_length_hist.png)

	## Training loss

	![Loss linear](docs/colab_l4_bf16_loss.png)

	![Loss log scale](docs/colab_l4_bf16_loss_log.png)

	## Inference

	```python
	from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
	import torch

	model_id = "pymlex/ipa-transcriptor-300M"
	source_prefix = "ipa: "

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
	model.eval()
	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
	model.to(device)

	def transcribe(word: str, num_beams: int = 4) -> str:
	source = f"{source_prefix}{word.strip().lower()}"
	encoded = tokenizer(source, return_tensors="pt", truncation=True, max_length=36).to(device)
	output_ids = model.generate(**encoded, max_new_tokens=56, num_beams=num_beams, early_stopping=True)
	return tokenizer.decode(output_ids[0], skip_special_tokens=True)

	print(transcribe("analytical"))
	```