Instructions to use VERSIL91/pretrain_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VERSIL91/pretrain_model_2 with Transformers:

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

pipe = pipeline("text-generation", model="VERSIL91/pretrain_model_2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VERSIL91/pretrain_model_2")
model = AutoModelForCausalLM.from_pretrained("VERSIL91/pretrain_model_2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VERSIL91/pretrain_model_2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VERSIL91/pretrain_model_2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VERSIL91/pretrain_model_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VERSIL91/pretrain_model_2

SGLang

How to use VERSIL91/pretrain_model_2 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 "VERSIL91/pretrain_model_2" \
    --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": "VERSIL91/pretrain_model_2",
		"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 "VERSIL91/pretrain_model_2" \
        --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": "VERSIL91/pretrain_model_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VERSIL91/pretrain_model_2 with Docker Model Runner:
```
docker model run hf.co/VERSIL91/pretrain_model_2
```

pretrain_model_2

File size: 1,088 Bytes

e0fc12a

{
  "_name_or_path": "/root/.ml/pretraining/local-models/training/2024-02-05_13-38-08/",
  "activation_dropout": 0.0,
  "activation_function": "gelu",
  "architectures": [
    "BartForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 0,
  "classifier_dropout": 0.0,
  "d_model": 768,
  "decoder_attention_heads": 12,
  "decoder_ffn_dim": 3072,
  "decoder_layerdrop": 0.0,
  "decoder_layers": 15,
  "decoder_start_token_id": 2,
  "dropout": 0.1,
  "encoder_attention_heads": 12,
  "encoder_ffn_dim": 3072,
  "encoder_layerdrop": 0.0,
  "encoder_layers": 15,
  "eos_token_id": 2,
  "forced_eos_token_id": 2,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1",
    "2": "LABEL_2"
  },
  "init_std": 0.02,
  "is_decoder": true,
  "is_encoder_decoder": false,
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1,
    "LABEL_2": 2
  },
  "max_position_embeddings": 1024,
  "model_type": "bart",
  "num_hidden_layers": 15,
  "pad_token_id": 1,
  "scale_embedding": false,
  "torch_dtype": "float32",
  "transformers_version": "4.34.1",
  "use_cache": true,
  "vocab_size": 50265
}