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", device_map="auto")

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
```

tomaarsen HF Staff commited on Jun 19

Commit

fb2e560

verified ·

1 Parent(s): 28a8190

Rename model

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -7,7 +7,7 @@ tags:
 - lora
 ---
-# tomaarsen/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`**.
@@ -20,7 +20,7 @@ are stored under the old `language_model.model.layers.*` layout, so loading them
 from transformers import PaliGemmaModel
 model = PaliGemmaModel.from_pretrained(
-    "tomaarsen/tiny-random-paligemma-lora-key-mapping",
     key_mapping={r"language_model\.model\.": "language_model."},
 )
 ```

 - 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`**.
 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."},
 )
 ```