Instructions to use Tanmay09516/llama-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tanmay09516/llama-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Trelis/Llama-2-7b-chat-hf-sharded-bf16") model = PeftModel.from_pretrained(base_model, "Tanmay09516/llama-sql") - Notebooks
- Google Colab
- Kaggle
File size: 460 Bytes
ccb873a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"auto_mapping": null,
"base_model_name_or_path": "Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 16,
"lora_dropout": 0.1,
"modules_to_save": null,
"peft_type": "LORA",
"r": 64,
"revision": null,
"target_modules": [
"q_proj",
"v_proj"
],
"task_type": "CAUSAL_LM"
} |