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, AutoModelForSeq2SeqLM

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

File size: 846 Bytes

4fdfadf

{
  "architectures": [
    "T5ForConditionalGeneration"
  ],
  "classifier_dropout": 0.0,
  "d_ff": 3584,
  "d_kv": 64,
  "d_model": 1472,
  "decoder_start_token_id": 0,
  "dense_act_fn": "gelu_new",
  "dropout_rate": 0.1,
  "dtype": "float32",
  "eos_token_id": 1,
  "feed_forward_proj": "gated-gelu",
  "gradient_checkpointing": false,
  "initializer_factor": 1.0,
  "is_decoder": false,
  "is_encoder_decoder": true,
  "is_gated_act": true,
  "layer_norm_epsilon": 1e-06,
  "model_type": "t5",
  "num_decoder_layers": 4,
  "num_heads": 6,
  "num_layers": 12,
  "pad_token_id": 0,
  "relative_attention_max_distance": 128,
  "relative_attention_num_buckets": 32,
  "scale_decoder_outputs": true,
  "tie_word_embeddings": true,
  "tokenizer_class": "ByT5Tokenizer",
  "transformers_version": "5.0.0",
  "use_cache": false,
  "vocab_size": 384
}