Instructions to use Gustrd/mpt-7b-lora-cabra3-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Gustrd/mpt-7b-lora-cabra3-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HachiML/mpt-7b-instruct-for-peft") model = PeftModel.from_pretrained(base_model, "Gustrd/mpt-7b-lora-cabra3-adapter") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Gustrd/mpt-7b-lora-cabra3-adapter: direct link, hf CLI and curl.
- Browser
- Download file 936 Bytes
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https://huggingface.co/Gustrd/mpt-7b-lora-cabra3-adapter/resolve/8ac1db557560c574286c3ac581163effc3e8f1fb/README.md
- Command line
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hf download hf://Gustrd/mpt-7b-lora-cabra3-adapter@8ac1db557560c574286c3ac581163effc3e8f1fb/README.md
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curl -L -o README.md https://huggingface.co/Gustrd/mpt-7b-lora-cabra3-adapter/resolve/8ac1db557560c574286c3ac581163effc3e8f1fb/README.md
936 Bytes
metadata
language:
- pt
license: cc-by-3.0
library_name: peft
datasets:
- dominguesm/wikipedia-ptbr-20230601
base_model: HachiML/mpt-7b-instruct-for-peft
Cabra: A portuguese finetuned instruction commercial model
LoRA adapter created with the procedures detailed at the GitHub repository: https://github.com/gustrd/cabra .
This training was done at 1 epoch using P100 at Kaggle, by around 11 hours, at a random slice of the dataset.
This LoRA adapter was created following the procedure:
Training procedure
The following bitsandbytes quantization config was used during training:
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
- PEFT 0.5.0.dev0