Instructions to use lusstta/LLaMa2-Qlora-AiresAi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lusstta/LLaMa2-Qlora-AiresAi with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "lusstta/LLaMa2-Qlora-AiresAi") - Notebooks
- Google Colab
- Kaggle
About
Adapter train using Qlora made for LLaMa2 7b Chat. This adapter adds the ability to fully, fluently and uninterruptedly speak other languages to LLaMa2.
Training procedure
The following bitsandbytes quantization config was used during training:
- 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: False
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.5.0.dev0
- Downloads last month
- 2
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Model tree for lusstta/LLaMa2-Qlora-AiresAi
Base model
TinyPixel/Llama-2-7B-bf16-sharded