Text Generation
PEFT
Safetensors
medical
healthcare
maternal-health
sexual-health
reproductive-health
multilingual
african-languages
akan
amharic
luganda
swahili
lora
medgemma
conversational
Instructions to use KYAGABA/testmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KYAGABA/testmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-27b-text-it") model = PeftModel.from_pretrained(base_model, "KYAGABA/testmodel") - Notebooks
- Google Colab
- Kaggle
File size: 751 Bytes
335708f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"alpha_pattern": {},
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"base_model_name_or_path": "google/medgemma-27b-text-it",
"bias": "none",
"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": 16,
"lora_bias": false,
"lora_dropout": 0.05,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 8,
"rank_pattern": {},
"revision": null,
"target_modules": [
"o_proj",
"k_proj",
"q_proj",
"v_proj"
],
"task_type": "CAUSAL_LM",
"use_dora": false,
"use_rslora": false
} |