Instructions to use BigSalmon/ConvertLowercaseToUppercase3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/ConvertLowercaseToUppercase3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/ConvertLowercaseToUppercase3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/ConvertLowercaseToUppercase3") model = AutoModelForCausalLM.from_pretrained("BigSalmon/ConvertLowercaseToUppercase3") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BigSalmon/ConvertLowercaseToUppercase3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/ConvertLowercaseToUppercase3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/ConvertLowercaseToUppercase3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/ConvertLowercaseToUppercase3
- SGLang
How to use BigSalmon/ConvertLowercaseToUppercase3 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 "BigSalmon/ConvertLowercaseToUppercase3" \ --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": "BigSalmon/ConvertLowercaseToUppercase3", "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 "BigSalmon/ConvertLowercaseToUppercase3" \ --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": "BigSalmon/ConvertLowercaseToUppercase3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/ConvertLowercaseToUppercase3 with Docker Model Runner:
docker model run hf.co/BigSalmon/ConvertLowercaseToUppercase3
File size: 1,287 Bytes
c26aaca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | {
"_name_or_path": "xhyi/PT_GPTNEO350_ATG",
"activation_function": "gelu_new",
"architectures": [
"GPTNeoForCausalLM"
],
"attention_dropout": 0,
"attention_layers": [
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local"
],
"attention_types": [
[
[
"global",
"local"
],
12
]
],
"bos_token_id": 50256,
"embed_dropout": 0,
"eos_token_id": 50256,
"gradient_checkpointing": false,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": null,
"layer_norm_epsilon": 1e-05,
"max_position_embeddings": 2048,
"model_friendly_id": "PT_GPTNEO350_ATG",
"model_type": "gpt_neo",
"num_heads": 16,
"num_layers": 24,
"resid_dropout": 0,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "float32",
"transformers_version": "4.26.0.dev0",
"use_cache": true,
"vocab_size": 50257,
"window_size": 256
}
|