Instructions to use majorSeaweed/Pokemon-Image-Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majorSeaweed/Pokemon-Image-Captioning with Transformers:

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

pipe = pipeline("image-text-to-text", model="majorSeaweed/Pokemon-Image-Captioning")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("majorSeaweed/Pokemon-Image-Captioning")
model = AutoModelForImageTextToText.from_pretrained("majorSeaweed/Pokemon-Image-Captioning")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use majorSeaweed/Pokemon-Image-Captioning with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/majorSeaweed/Pokemon-Image-Captioning

SGLang

How to use majorSeaweed/Pokemon-Image-Captioning 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 "majorSeaweed/Pokemon-Image-Captioning" \
    --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": "majorSeaweed/Pokemon-Image-Captioning",
		"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 "majorSeaweed/Pokemon-Image-Captioning" \
        --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": "majorSeaweed/Pokemon-Image-Captioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use majorSeaweed/Pokemon-Image-Captioning with Docker Model Runner:
```
docker model run hf.co/majorSeaweed/Pokemon-Image-Captioning
```

majorSeaweed commited on Jan 31, 2025

Commit

7e1d0b5

verified ·

1 Parent(s): ed2e57d

Upload processor

Browse files

Files changed (1) hide show

tokenizer_config.json +7 -0

tokenizer_config.json CHANGED Viewed

@@ -47,17 +47,24 @@
   "do_lower_case": true,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
   "model_input_names": [
     "input_ids",
     "attention_mask"
   ],
   "model_max_length": 512,
   "never_split": null,
   "pad_token": "[PAD]",
   "processor_class": "BlipProcessor",
   "sep_token": "[SEP]",
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "BertTokenizer",
   "unk_token": "[UNK]"
 }

   "do_lower_case": true,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
+  "max_length": 128,
   "model_input_names": [
     "input_ids",
     "attention_mask"
   ],
   "model_max_length": 512,
   "never_split": null,
+  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "processor_class": "BlipProcessor",
   "sep_token": "[SEP]",
+  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }