Instructions to use liujch1998/rainier-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liujch1998/rainier-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="liujch1998/rainier-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("liujch1998/rainier-large") model = AutoModelForSeq2SeqLM.from_pretrained("liujch1998/rainier-large") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use liujch1998/rainier-large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "liujch1998/rainier-large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "liujch1998/rainier-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/liujch1998/rainier-large
- SGLang
How to use liujch1998/rainier-large 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 "liujch1998/rainier-large" \ --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": "liujch1998/rainier-large", "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 "liujch1998/rainier-large" \ --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": "liujch1998/rainier-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use liujch1998/rainier-large with Docker Model Runner:
docker model run hf.co/liujch1998/rainier-large
Commit ·
c8a4186
1
Parent(s): c1468bc
add model
Browse files- config.json +57 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "t5-large",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 4096,
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"d_kv": 64,
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"d_model": 1024,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 24,
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"num_heads": 16,
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"num_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.16.2",
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"use_cache": true,
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"vocab_size": 32128
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:01e989b256655b2b60ae10db716596541d0d0f22aedf580e2d0afc508d6ac10c
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size 2950909652
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