Instructions to use afrias5/meta-codellama-34b-python-Score8192V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use afrias5/meta-codellama-34b-python-Score8192V4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/CodeLlama-34b-Python-hf") model = PeftModel.from_pretrained(base_model, "afrias5/meta-codellama-34b-python-Score8192V4") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: llama2 | |
| base_model: meta-llama/CodeLlama-34b-Python-hf | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| datasets: | |
| - afrias5/datasetScoreFinal | |
| model-index: | |
| - name: meta-codellama-34b-python-Score8192V4 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.5.3.dev41+g5e9fa33f` | |
| ```yaml | |
| base_model: meta-llama/CodeLlama-34b-Python-hf | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: CodeLlamaTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: afrias5/datasetScoreFinal | |
| type: alpaca | |
| field: text | |
| # dataset_prepared_path: ./FinUpTagsNoTestNoExNew | |
| val_set_size: 0 | |
| output_dir: models/meta-codellama-34b-python-Score8192V4 | |
| lora_model_dir: models/meta-codellama-34b-python-Score8192V4/checkpoint-55 | |
| auto_resume_from_checkpoints: true | |
| sequence_len: 8192 | |
| sample_packing: true | |
| pad_to_sequence_len: true | |
| eval_sample_packing: False | |
| adapter: lora | |
| lora_model_dir: | |
| lora_r: 2 | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_target_linear: true | |
| lora_fan_in_fan_out: | |
| lora_modules_to_save: | |
| - embed_tokens | |
| - lm_head | |
| wandb_project: 'Code34bNewFeed' | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_run_id: | |
| wandb_name: 'meta-codellama-34b-python-Score8192V4' | |
| wandb_log_model: | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 1 | |
| num_epochs: 14 | |
| optimizer: adamw_torch | |
| lr_scheduler: cosine | |
| learning_rate: 0.0002 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: true | |
| fp16: | |
| tf32: false | |
| hub_model_id: afrias5/meta-codellama-34b-python-Score8192V4 | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: false | |
| s2_attention: | |
| logging_steps: 1 | |
| warmup_steps: 10 | |
| saves_per_epoch: 1 | |
| save_total_limit: 16 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_all.json | |
| fsdp_config: | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "</s>" | |
| unk_token: "<unk>" | |
| ``` | |
| </details><br> | |
| # meta-codellama-34b-python-Score8192V4 | |
| This model is a fine-tuned version of [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) on the afrias5/datasetScoreFinal dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 2 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 8 | |
| - total_eval_batch_size: 2 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - num_epochs: 14 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.46.3 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 |