---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7f8bab48-3ea0-4b73-b5ea-dfe9183daaf2
  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-135M
bf16: true
chat_template: llama3
dataloader_num_workers: 24
dataset_prepared_path: null
datasets:
- data_files:
  - 5b80cb3e3f6cb6d6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/5b80cb3e3f6cb6d6_train_data.json
  type:
    field_input: outline
    field_instruction: topic
    field_output: markdown
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: nttx/7f8bab48-3ea0-4b73-b5ea-dfe9183daaf2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 64
lora_dropout: 0.05
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: 3000
micro_batch_size: 2
mlflow_experiment_name: /tmp/5b80cb3e3f6cb6d6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f8675f5c-3a6e-463d-b56c-c54c0872a08f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f8675f5c-3a6e-463d-b56c-c54c0872a08f
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 7f8bab48-3ea0-4b73-b5ea-dfe9183daaf2

This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7823

## 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: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 3000

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0015 | 1    | 1.1010          |
| 0.9411        | 0.2226 | 150  | 0.9269          |
| 0.9052        | 0.4452 | 300  | 0.8922          |
| 0.8705        | 0.6678 | 450  | 0.8678          |
| 0.8544        | 0.8904 | 600  | 0.8514          |
| 0.8167        | 1.1132 | 750  | 0.8398          |
| 0.7961        | 1.3357 | 900  | 0.8281          |
| 0.7965        | 1.5583 | 1050 | 0.8186          |
| 0.7986        | 1.7809 | 1200 | 0.8100          |
| 0.7548        | 2.0037 | 1350 | 0.8026          |
| 0.7409        | 2.2263 | 1500 | 0.7998          |
| 0.7508        | 2.4489 | 1650 | 0.7954          |
| 0.7606        | 2.6715 | 1800 | 0.7908          |
| 0.7366        | 2.8941 | 1950 | 0.7873          |
| 0.7173        | 3.1169 | 2100 | 0.7872          |
| 0.7294        | 3.3395 | 2250 | 0.7847          |
| 0.7282        | 3.5620 | 2400 | 0.7835          |
| 0.72          | 3.7846 | 2550 | 0.7826          |
| 0.7085        | 4.0074 | 2700 | 0.7819          |
| 0.7127        | 4.2300 | 2850 | 0.7822          |
| 0.7119        | 4.4526 | 3000 | 0.7823          |


### Framework versions

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