Instructions to use SALT-NLP/CultureBank-Mixtral-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SALT-NLP/CultureBank-Mixtral-DPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/sphinx/u/culturebank/models/checkpoints/mixtral_cultural/sft_train_v0/final_merged_checkpoint") model = PeftModel.from_pretrained(base_model, "SALT-NLP/CultureBank-Mixtral-DPO") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from SALT-NLP/CultureBank-Mixtral-DPO: direct link, hf CLI and curl.
- Browser
- Download file 484 Bytes
-
https://huggingface.co/SALT-NLP/CultureBank-Mixtral-DPO/resolve/main/README.md
- Command line
-
hf download hf://SALT-NLP/CultureBank-Mixtral-DPO/README.md
-
curl -L -o README.md https://huggingface.co/SALT-NLP/CultureBank-Mixtral-DPO/resolve/main/README.md
484 Bytes
metadata
library_name: peft
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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
Framework versions
- PEFT 0.5.0