Instructions to use DeepaPeri/doctor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DeepaPeri/doctor with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "DeepaPeri/doctor") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from DeepaPeri/doctor: direct link, hf CLI and curl.
- Browser
- Download file 435 Bytes
-
https://huggingface.co/DeepaPeri/doctor/resolve/main/README.md
- Command line
-
hf download hf://DeepaPeri/doctor/README.md
-
curl -L -o README.md https://huggingface.co/DeepaPeri/doctor/resolve/main/README.md
435 Bytes
metadata
library_name: peft
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 0.4.0