| library_name: peft | |
| tags: | |
| - Llama2 | |
| - code | |
| - llama | |
| - opensource | |
| pipeline_tag: text-generation | |
| inference: true | |
| base_model: codellama/CodeLlama-34b-Instruct-hf | |
| ## Aria Code is based on Code LLAMA 34B Instruct finetuned on French | |
| Aria code is a model built for educational purposes in French speaking countries on coding. | |
| The model is built to help students to learn coding in their native language while having the base qualities of LLAMA CODE which has been | |
| trained over 500 billions token of coding content. We belive coding skills are very valuable to reduce youth unemployment rates around the world | |
| and gives more scientific skills to students. LLAMA 2 base models have enough safeguards and censorship to ensure a safe use by kids and within academic environments. | |
| GPU used for training: Nvidia A100 | |
| Timing: Less than 24 hours | |
| Number of finetuning tokens : Over 10.000 high quality french language tokens. | |
| ## 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 |