Instructions to use mrapacz/interlinear-pl-philta-emb-auto-normalized-ob with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrapacz/interlinear-pl-philta-emb-auto-normalized-ob with Transformers:

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

pipe = pipeline("text-generation", model="mrapacz/interlinear-pl-philta-emb-auto-normalized-ob")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("mrapacz/interlinear-pl-philta-emb-auto-normalized-ob", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mrapacz/interlinear-pl-philta-emb-auto-normalized-ob with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrapacz/interlinear-pl-philta-emb-auto-normalized-ob

SGLang

How to use mrapacz/interlinear-pl-philta-emb-auto-normalized-ob 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 "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob" \
    --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": "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob",
		"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 "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob" \
        --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": "mrapacz/interlinear-pl-philta-emb-auto-normalized-ob",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrapacz/interlinear-pl-philta-emb-auto-normalized-ob with Docker Model Runner:
```
docker model run hf.co/mrapacz/interlinear-pl-philta-emb-auto-normalized-ob
```

interlinear-pl-philta-emb-auto-normalized-ob / config.json

mrapacz

Upload MorphT5AutoForConditionalGeneration

825ec46 verified about 1 year ago

raw

history blame contribute delete

995 Bytes

	{
	"_name_or_path": "../workspaces/exp512_3_INTMT-6712/best_model",
	"architectures": [
	"MorphT5AutoForConditionalGeneration"
	],
	"d_ff": 2048,
	"d_kv": 64,
	"d_model": 768,
	"decoder_start_token_id": 0,
	"dense_act_fn": "gelu_new",
	"dropout_rate": 0.1,
	"eos_token_id": 1,
	"feed_forward_proj": "gated-gelu",
	"gradient_checkpointing": false,
	"initializer_factor": 1.0,
	"is_encoder_decoder": true,
	"is_gated_act": true,
	"layer_norm_epsilon": 1e-06,
	"model_type": "morph-t5-auto",
	"morph_compressed_embedding_size": 64,
	"morph_vocabulary_size": 1074,
	"num_decoder_layers": 12,
	"num_heads": 12,
	"num_layers": 12,
	"output_past": true,
	"pad_token_id": 0,
	"relative_attention_max_distance": 128,
	"relative_attention_num_buckets": 32,
	"tie_word_embeddings": false,
	"tokenizer_class": "morpht5.tokenizer.morph_t5_tokenizer.MorphT5Tokenizer",
	"torch_dtype": "float32",
	"transformers_version": "4.31.0",
	"use_cache": true,
	"vocab_size": 64103
	}