Text Generation
Transformers
PyTorch
TensorBoard
llama
Generated from Trainer
text-generation-inference
Instructions to use AravindAct/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AravindAct/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AravindAct/output")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AravindAct/output") model = AutoModelForCausalLM.from_pretrained("AravindAct/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AravindAct/output with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AravindAct/output" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AravindAct/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AravindAct/output
- SGLang
How to use AravindAct/output 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 "AravindAct/output" \ --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": "AravindAct/output", "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 "AravindAct/output" \ --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": "AravindAct/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AravindAct/output with Docker Model Runner:
docker model run hf.co/AravindAct/output
Commit ·
ba36ccc
1
Parent(s): 08a812d
Upload 2 files
Browse files- config.json +6 -6
config.json
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{
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"_name_or_path": "t5-
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff":
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"d_kv": 64,
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"d_model":
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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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":
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"num_heads":
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"num_layers":
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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{
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"_name_or_path": "t5-base",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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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": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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