--- library_name: transformers base_model: meta-llama/Llama-3.2-3B tags: - generated_from_trainer model-index: - name: 22b-fft-out results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: meta-llama/Llama-3.2-3B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false datasets: # - path: anthracite-core/c2_logs_32k_mistral-v3_v1.2 # type: sharegpt # conversation: chatml - path: ./datasets/c2_deduped_32k_mistral-v3_tok_deanon_dsclean_1.2.jsonl type: sharegpt conversation: chatml # - path: anthracite-org/kalo-opus-instruct-22k-no-refusal # type: sharegpt # conversation: chatml - path: ./datasets/opus-instruct-22k-no_refusals.jsonl type: sharegpt conversation: chatml # - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered # type: sharegpt # conversation: chatml - path: ./datasets/kalo-3k-filtered.jsonl type: sharegpt conversation: chatml # - path: anthracite-org/nopm_claude_writing_fixed # type: sharegpt # conversation: chatml - path: ./datasets/claudewritingNopm.jsonl type: sharegpt conversation: chatml # - path: anthracite-org/kalo_opus_misc_240827 # type: sharegpt # conversation: chatml - path: ./datasets/kalo_opus_misc_240827.jsonl type: sharegpt conversation: chatml # - path: anthracite-org/kalo_misc_part2 # type: sharegpt # conversation: chatml - path: ./datasets/kalo_misc_part2.jsonl type: sharegpt conversation: chatml # - path: NewEden/Claude-Instruct-5K # type: sharegpt # conversation: chatml - path: ./datasets/5k.jsonl type: sharegpt conversation: chatml #chat_template: chatml shuffle_merged_datasets: true #default_system_message: "You are an assistant that responds to the user." dataset_prepared_path: ./magnum-22b-data val_set_size: 0.0 output_dir: ./22b-fft-out sequence_len: 16000 sample_packing: true pad_to_sequence_len: true wandb_project: 3bmagnum wandb_entity: wandb_watch: wandb_name: 3magnum wandb_log_model: gradient_accumulation_steps: 32 micro_batch_size: 1 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 40 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 debug: #deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: pad_token: <|finetune_right_pad_id|> ```

# 22b-fft-out This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co./meta-llama/Llama-3.2-3B) on the None 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: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1