Instructions to use Gabriel2502/NIH_GPT2_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gabriel2502/NIH_GPT2_Classifier with Transformers:

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

pipe = pipeline("image-text-to-text", model="Gabriel2502/NIH_GPT2_Classifier")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("Gabriel2502/NIH_GPT2_Classifier")
model = AutoModelForImageTextToText.from_pretrained("Gabriel2502/NIH_GPT2_Classifier")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gabriel2502/NIH_GPT2_Classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gabriel2502/NIH_GPT2_Classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gabriel2502/NIH_GPT2_Classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gabriel2502/NIH_GPT2_Classifier

SGLang

How to use Gabriel2502/NIH_GPT2_Classifier 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 "Gabriel2502/NIH_GPT2_Classifier" \
    --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": "Gabriel2502/NIH_GPT2_Classifier",
		"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 "Gabriel2502/NIH_GPT2_Classifier" \
        --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": "Gabriel2502/NIH_GPT2_Classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gabriel2502/NIH_GPT2_Classifier with Docker Model Runner:
```
docker model run hf.co/Gabriel2502/NIH_GPT2_Classifier
```

NIH_GPT2_Classifier / config.json

Gabriel2502

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	{
	"architectures": [
	"VisionEncoderDecoderModel"
	],
	"bos_token_id": 50256,
	"decoder": {
	"activation_function": "gelu_new",
	"add_cross_attention": true,
	"architectures": [
	"GPT2LMHeadModel"
	],
	"attn_pdrop": 0.1,
	"decoder_start_token_id": 50256,
	"embd_pdrop": 0.1,
	"initializer_range": 0.02,
	"is_decoder": true,
	"layer_norm_epsilon": 1e-05,
	"model_type": "gpt2",
	"n_ctx": 1024,
	"n_embd": 768,
	"n_head": 12,
	"n_inner": null,
	"n_layer": 12,
	"n_positions": 1024,
	"pad_token_id": 50256,
	"reorder_and_upcast_attn": false,
	"resid_pdrop": 0.1,
	"scale_attn_by_inverse_layer_idx": false,
	"scale_attn_weights": true,
	"summary_activation": null,
	"summary_first_dropout": 0.1,
	"summary_proj_to_labels": true,
	"summary_type": "cls_index",
	"summary_use_proj": true,
	"task_specific_params": {
	"text-generation": {
	"do_sample": true,
	"max_length": 50
	}
	},
	"torch_dtype": "float32",
	"use_cache": true,
	"vocab_size": 50257
	},
	"decoder_start_token_id": 50256,
	"encoder": {
	"architectures": [
	"ViTModel"
	],
	"attention_probs_dropout_prob": 0.0,
	"encoder_stride": 16,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.0,
	"hidden_size": 768,
	"image_size": 224,
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"layer_norm_eps": 1e-12,
	"model_type": "vit",
	"num_attention_heads": 12,
	"num_channels": 3,
	"num_hidden_layers": 12,
	"patch_size": 16,
	"pooler_act": "tanh",
	"pooler_output_size": 768,
	"qkv_bias": true,
	"torch_dtype": "float32"
	},
	"eos_token_id": 50256,
	"is_encoder_decoder": true,
	"model_type": "vision-encoder-decoder",
	"pad_token_id": 50256,
	"tie_word_embeddings": false,
	"torch_dtype": "float32",
	"transformers_version": "4.51.3"
	}