Instructions to use nakcnx/typhoon-sql-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nakcnx/typhoon-sql-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/typhoon-7b") model = PeftModel.from_pretrained(base_model, "nakcnx/typhoon-sql-qlora") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
base_model: scb10x/typhoon-7b
datasets:
- b-mc2/sql-create-context
language:
- en
- th
Model Description
Typhoon-7b QLoRA Finetune by unsloth with SQL Context dataset.
Training Hyperparameters
Batch Size: 48 (4(BS)x4(GAS)x3(GPU))
The following bitsandbytes quantization config was used during training:
- r = 64
- lora_alpha = 16
- lora_dropout = 0.05
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
LOSS
Step Training Loss Eval Loss
1550 (Epoch:1) 0.4295 0.4367
3110 (Epoch:2) 0.4057 0.4217
Framework versions
- PEFT 0.7.0