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](https://github.com/axolotl-ai-cloud/axolotl)

See axolotl config

axolotl version: `0.8.0.dev0` ```yaml base_model: meta-llama/Llama-3.1-8B-Instruct # optionally might have model_type or tokenizer_type model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer # Automatically upload checkpoint and final model to HF hub_model_id: seacorn/llama3.1-8b-reasoning-summarizer load_in_8bit: true load_in_4bit: false strict: false seed: 42 datasets: - path: output.jsonl type: chat_template dataset_prepared_path: val_set_size: 0.05 output_dir: ./lora-out sequence_len: 8192 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 16 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_modules_to_save: - embed_tokens - lm_head wandb_project: huggingface wandb_entity: wandb_watch: wandb_name: llama3.1-8b-reasoning-summarizer wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 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 s2_attention: warmup_ratio: 0.05 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 5 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: <|end_of_text|> ```

# llama3.1-8b-reasoning-summarizer This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [seacorn/news-summarizer-reasoner](https://huggingface.co/datasets/seacorn/news-summarizer-reasoner) dataset. It achieves the following results on the evaluation set: - Loss: 1.1173 ## Intended uses & limitations The model performs best in summarization tasks, specifically in English and maybe Chinese. The model provides reasoning ON/OFF via system prompt trigger, all instructions should be contained within the user prompt. Reasoning on example: ```json messages = [ {"role": "system", "content": "reasoning on"}, {"role": "user", "content": "Summarize the following into 5 bullet points, each with 20 words max.\n\nMarch 28 (Reuters) -..."} ] # output - Elon Musk's xAI acquires X ... ``` Reasoning off example: ```json messages = [ {"role": "system", "content": "reasoning off"}, {"role": "user", "content": "Summarize the following into 5 bullet points, each with 20 words max.\n\nMarch 28 (Reuters) -..."} ] # output Okay, I need to summarize this article into 5 bullet points, each with a maximum of 20 words. ... - Musk's xAI acquires X ... ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB 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: 56 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0396 | 0.0018 | 1 | 1.7982 | | 1.3908 | 0.2506 | 141 | 1.2241 | | 1.8534 | 0.5011 | 282 | 1.1842 | | 1.5745 | 0.7517 | 423 | 1.1560 | | 0.9261 | 1.0018 | 564 | 1.1288 | | 1.2359 | 1.2523 | 705 | 1.1344 | | 1.1835 | 1.5029 | 846 | 1.1223 | | 0.9898 | 1.7534 | 987 | 1.1173 | ### Framework versions - PEFT 0.15.0 - Transformers 4.50.0 - Pytorch 2.5.1+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1