---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-360M-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b560dce-4b9a-4461-bec5-e809c217723b
  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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-360M-Instruct
bf16: auto
chat_template: llama3
dataloader_num_workers: 6
dataset_prepared_path: null
datasets:
- data_files:
  - 824ef92913906b02_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/824ef92913906b02_train_data.json
  type:
    field_input: gt_answer
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: error577/3b560dce-4b9a-4461-bec5-e809c217723b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2000
micro_batch_size: 2
mlflow_experiment_name: /tmp/824ef92913906b02_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.02
wandb_entity: null
wandb_mode: online
wandb_name: edbc27c0-2567-4ef1-849f-694fac00cd13
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: edbc27c0-2567-4ef1-849f-694fac00cd13
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 3b560dce-4b9a-4461-bec5-e809c217723b

This model is a fine-tuned version of [unsloth/SmolLM-360M-Instruct](https://huggingface.co/unsloth/SmolLM-360M-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3736

## 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: 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: 10
- training_steps: 2000

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6086        | 0.0001 | 1    | 0.8242          |
| 0.6491        | 0.0069 | 50   | 0.5668          |
| 0.4904        | 0.0137 | 100  | 0.5020          |
| 0.4928        | 0.0206 | 150  | 0.4803          |
| 0.3963        | 0.0275 | 200  | 0.4617          |
| 0.4291        | 0.0343 | 250  | 0.4514          |
| 0.4164        | 0.0412 | 300  | 0.4411          |
| 0.4261        | 0.0481 | 350  | 0.4344          |
| 0.4734        | 0.0549 | 400  | 0.4273          |
| 0.4271        | 0.0618 | 450  | 0.4246          |
| 0.3362        | 0.0687 | 500  | 0.4193          |
| 0.3781        | 0.0755 | 550  | 0.4136          |
| 0.4247        | 0.0824 | 600  | 0.4107          |
| 0.3431        | 0.0893 | 650  | 0.4071          |
| 0.3674        | 0.0961 | 700  | 0.4019          |
| 0.4549        | 0.1030 | 750  | 0.4004          |
| 0.4299        | 0.1098 | 800  | 0.3972          |
| 0.3398        | 0.1167 | 850  | 0.3938          |
| 0.4542        | 0.1236 | 900  | 0.3924          |
| 0.301         | 0.1304 | 950  | 0.3908          |
| 0.4517        | 0.1373 | 1000 | 0.3881          |
| 0.4243        | 0.1442 | 1050 | 0.3865          |
| 0.3469        | 0.1510 | 1100 | 0.3853          |
| 0.3413        | 0.1579 | 1150 | 0.3834          |
| 0.3609        | 0.1648 | 1200 | 0.3815          |
| 0.3546        | 0.1716 | 1250 | 0.3813          |
| 0.2967        | 0.1785 | 1300 | 0.3794          |
| 0.3278        | 0.1854 | 1350 | 0.3783          |
| 0.3304        | 0.1922 | 1400 | 0.3782          |
| 0.2565        | 0.1991 | 1450 | 0.3774          |
| 0.3721        | 0.2060 | 1500 | 0.3764          |
| 0.4291        | 0.2128 | 1550 | 0.3754          |
| 0.299         | 0.2197 | 1600 | 0.3757          |
| 0.4525        | 0.2266 | 1650 | 0.3750          |
| 0.3902        | 0.2334 | 1700 | 0.3747          |
| 0.3118        | 0.2403 | 1750 | 0.3737          |
| 0.3434        | 0.2472 | 1800 | 0.3747          |
| 0.3902        | 0.2540 | 1850 | 0.3732          |
| 0.3748        | 0.2609 | 1900 | 0.3742          |
| 0.3463        | 0.2678 | 1950 | 0.3742          |
| 0.3441        | 0.2746 | 2000 | 0.3736          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1