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 / adapter_config.json

tomaarsen HF Staff

Add tiny-random PaliGemma LoRA fixture requiring key_mapping

db282bf verified 19 days ago

Raw

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1.27 kB

	{
	"alora_invocation_tokens": null,
	"alpha_pattern": {},
	"arrow_config": null,
	"auto_mapping": {
	"base_model_class": "PaliGemmaForConditionalGeneration",
	"parent_library": "transformers.models.paligemma.modeling_paligemma"
	},
	"base_model_name_or_path": "tomaarsen/tiny-random-paligemma-lora-key-mapping",
	"bias": "none",
	"corda_config": null,
	"ensure_weight_tying": false,
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": false,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 8,
	"lora_bias": false,
	"lora_dropout": 0.0,
	"lora_ga_config": null,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"peft_version": "0.19.2.dev0@76c37d4686cf7245a9a6998fd1e2619d62c9322d",
	"qalora_group_size": 16,
	"r": 8,
	"rank_pattern": {},
	"revision": null,
	"target_modules": ".language_model.\\.(q_proj\|v_proj)$",
	"target_parameters": null,
	"task_type": null,
	"trainable_token_indices": null,
	"use_bdlora": null,
	"use_dora": false,
	"use_qalora": false,
	"use_rslora": false
	}