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 / README.md

tomaarsen HF Staff

Rename model

fb2e560 verified 19 days ago

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	---
	library_name: transformers
	tags:
	- test-fixture
	- paligemma
	- peft
	- lora
	---

	# hf-internal-testing/tiny-random-paligemma-lora-key-mapping

	Tiny-random PaliGemma checkpoint bundling a LoRA adapter that **requires a `key_mapping` to load onto the
	underlying `PaliGemmaModel`**.

	It mirrors [`vidore/colpali`](https://huggingface.co/vidore/colpali) at tiny scale: the adapter's text weights
	are stored under the old `language_model.model.layers.*` layout, so loading them onto today's
	`PaliGemmaModel` (`language_model.layers.*`) needs:

	```python
	from transformers import PaliGemmaModel

	model = PaliGemmaModel.from_pretrained(
	"hf-internal-testing/tiny-random-paligemma-lora-key-mapping",
	key_mapping={r"language_model\.model\.": "language_model."},
	)
	```

	`PaliGemmaForConditionalGeneration` auto-bridges this (via the `llava` conversion) and does not need the
	mapping; the bare `PaliGemmaModel` does. Every `lora_A` weight is filled with `0.0234` and every
	`lora_B` weight with `0.0567`, so a test can assert the adapter was restored from the checkpoint.