README.md

---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- seacorn/news-summarizer-reasoner
model-index:
- name: llama3.1-8b-reasoning-summarizer
  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: `0.8.0.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|>

```

</details><br>

# 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 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
- Elon Musk's xAI acquires X ...
```

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
<think>
Okay, I need to summarize this article into 5 bullet points, each with a maximum of 20 words. ...
</think>

- 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