Image-Text-to-Text
Transformers
Safetensors
PEFT
paligemma
test-fixture
lora
text-generation-inference
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
- Transformers
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
Add tiny-random PaliGemma LoRA fixture requiring key_mapping
Browse files- README.md +33 -0
- adapter_config.json +43 -0
- adapter_model.safetensors +3 -0
- config.json +57 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
README.md
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---
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library_name: transformers
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tags:
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- test-fixture
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- paligemma
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- colpali
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- peft
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- lora
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---
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# tomaarsen/tiny-random-paligemma-lora-key-mapping
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Tiny-random PaliGemma checkpoint bundling a LoRA adapter that **requires a `key_mapping` to load onto the
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underlying `PaliGemmaModel`**.
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It mirrors [`vidore/colpali`](https://huggingface.co/vidore/colpali) at tiny scale: the adapter's text weights
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are stored under the old `language_model.model.layers.*` layout, so loading them onto today's
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`PaliGemmaModel` (`language_model.layers.*`) needs:
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```python
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from transformers import PaliGemmaModel
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model = PaliGemmaModel.from_pretrained(
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"tomaarsen/tiny-random-paligemma-lora-key-mapping",
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key_mapping={r"language_model\.model\.": "language_model."},
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)
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```
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`PaliGemmaForConditionalGeneration` auto-bridges this (via the `llava` conversion) and does not need the
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mapping; the bare `PaliGemmaModel` does. Every `lora_A` weight is filled with `0.0234` and every
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`lora_B` weight with `0.0567`, so a test can assert the adapter was restored from the checkpoint.
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Generated by `scripts/create_tiny_paligemma_lora_key_mapping_fixture.py`.
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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": {
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"base_model_class": "PaliGemmaForConditionalGeneration",
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"parent_library": "transformers.models.paligemma.modeling_paligemma"
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},
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"base_model_name_or_path": "tomaarsen/tiny-random-paligemma-lora-key-mapping",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": false,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 8,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.2.dev0@76c37d4686cf7245a9a6998fd1e2619d62c9322d",
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"qalora_group_size": 16,
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": ".*language_model.*\\.(q_proj|v_proj)$",
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"target_parameters": null,
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"task_type": null,
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fce525bfd50c235ff1c11b89ede25b0c1d047e7b9d637297b16492fa0b794e43
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size 7848
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config.json
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{
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"architectures": [
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"PaliGemmaForConditionalGeneration"
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],
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"dtype": "float32",
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"hidden_size": 2048,
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"image_token_index": 0,
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"model_type": "paligemma",
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"projection_dim": 32,
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"head_dim": 8,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 32,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"max_position_embeddings": 512,
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"model_type": "gemma",
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"num_attention_heads": 4,
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| 24 |
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"num_hidden_layers": 2,
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"num_image_tokens": 16,
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| 26 |
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"num_key_value_heads": 1,
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| 27 |
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"pad_token_id": 1,
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"rms_norm_eps": 1e-06,
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| 29 |
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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| 34 |
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"use_bidirectional_attention": true,
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"use_cache": true,
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"vocab_size": 99
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.13.0.dev0",
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"vision_config": {
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| 41 |
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"attention_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 32,
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"image_size": 20,
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"intermediate_size": 37,
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"layer_norm_eps": 1e-06,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 4,
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| 49 |
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"num_channels": 3,
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| 50 |
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"num_hidden_layers": 2,
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| 51 |
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"num_image_tokens": 4,
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"num_key_value_heads": 1,
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"patch_size": 5,
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"projection_dim": 32
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},
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"vocab_size": 257152
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"output_attentions": false,
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"output_hidden_states": false,
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| 7 |
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"pad_token_id": 1,
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"transformers_version": "5.13.0.dev0",
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"use_cache": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:6e323085a13881dd707a7d21ebc1d6ff968970c743dcea0158fec6ff292b3b36
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size 167508
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