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
```

pymlex commited on 13 days ago

Commit

28bf84e

verified ·

1 Parent(s): 9dfc71e

Update README.md

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README.md +6 -1

README.md CHANGED Viewed

@@ -8,6 +8,11 @@ tags:
 - seq2seq
 pipeline_tag: text-generation
 base_model: google/byt5-small
 ---
 # IPA Transcriptor 300M
@@ -73,4 +78,4 @@ def transcribe(word: str, num_beams: int = 4) -> str:
     return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 print(transcribe("analytical"))
-```

 - seq2seq
 pipeline_tag: text-generation
 base_model: google/byt5-small
+language:
+- en
+metrics:
+- perplexity
+- bleu
 ---
 # IPA Transcriptor 300M
     return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 print(transcribe("analytical"))
+```