Instructions to use haji80mr-uoft/medgemma-4b-it-only-merged-server-10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haji80mr-uoft/medgemma-4b-it-only-merged-server-10000 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("haji80mr-uoft/medgemma-4b-it-only-merged-server-10000", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "google/medgemma-4b-it", | |
| "bias": "none", | |
| "corda_config": null, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_bias": false, | |
| "lora_dropout": 0.1, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "qalora_group_size": 16, | |
| "r": 128, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "language_model.up_proj", | |
| "language_model.o_proj", | |
| "lm_heads.env_french", | |
| "language_model.v_proj", | |
| "lm_heads.env_chinese", | |
| "language_model.down_proj", | |
| "lm_heads.env_hindi", | |
| "language_model.k_proj", | |
| "lm_heads.env_spanish", | |
| "lm_heads.env_english", | |
| "language_model.q_proj", | |
| "language_model.gate_proj" | |
| ], | |
| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |