Instructions to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping with Transformers:

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

pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-random-paligemma-lora-key-mapping")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-paligemma-lora-key-mapping")
model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-paligemma-lora-key-mapping")

PEFT
How to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-paligemma-lora-key-mapping

SGLang

How to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping 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 "hf-internal-testing/tiny-random-paligemma-lora-key-mapping" \
    --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": "hf-internal-testing/tiny-random-paligemma-lora-key-mapping",
		"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 "hf-internal-testing/tiny-random-paligemma-lora-key-mapping" \
        --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": "hf-internal-testing/tiny-random-paligemma-lora-key-mapping",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-paligemma-lora-key-mapping with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-paligemma-lora-key-mapping
```

tiny-random-paligemma-lora-key-mapping / config.json

tomaarsen HF Staff

Add tiny-random PaliGemma LoRA fixture requiring key_mapping

db282bf verified 19 days ago

Raw

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1.52 kB

	{
	"architectures": [
	"PaliGemmaForConditionalGeneration"
	],
	"dtype": "float32",
	"hidden_size": 2048,
	"image_token_index": 0,
	"model_type": "paligemma",
	"projection_dim": 32,
	"text_config": {
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 2,
	"eos_token_id": 1,
	"head_dim": 8,
	"hidden_act": "gelu_pytorch_tanh",
	"hidden_activation": "gelu_pytorch_tanh",
	"hidden_size": 32,
	"initializer_range": 0.02,
	"intermediate_size": 37,
	"max_position_embeddings": 512,
	"model_type": "gemma",
	"num_attention_heads": 4,
	"num_hidden_layers": 2,
	"num_image_tokens": 16,
	"num_key_value_heads": 1,
	"pad_token_id": 1,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 10000.0,
	"rope_type": "default"
	},
	"tie_word_embeddings": true,
	"use_bidirectional_attention": true,
	"use_cache": true,
	"vocab_size": 99
	},
	"tie_word_embeddings": true,
	"transformers_version": "5.13.0.dev0",
	"vision_config": {
	"attention_dropout": 0.0,
	"hidden_act": "gelu_pytorch_tanh",
	"hidden_size": 32,
	"image_size": 20,
	"intermediate_size": 37,
	"layer_norm_eps": 1e-06,
	"model_type": "siglip_vision_model",
	"num_attention_heads": 4,
	"num_channels": 3,
	"num_hidden_layers": 2,
	"num_image_tokens": 4,
	"num_key_value_heads": 1,
	"patch_size": 5,
	"projection_dim": 32
	},
	"vocab_size": 257152
	}