Instructions to use willyninja30/ARIA_CODE_fr-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use willyninja30/ARIA_CODE_fr-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-34b-Instruct-hf") model = PeftModel.from_pretrained(base_model, "willyninja30/ARIA_CODE_fr-instruct") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: llama2
inference: true
datasets:
- Enno-Ai/fr-instructs
language:
- fr
- en
tags:
- code
- peft
- llama
- llama2
- codellama
pipeline_tag: text-generation
ARIA CODE FR Instruct is a finetuned model based on LLAMA CODE 34B INSTRUCT
This model has been trained over 10 Millions tokens on a french language dataset structured with Alpaca style.
GPU used for training : NVIDIA A100
Timing of training: 24H
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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 0.6.0.dev0