---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c06956a3-fe98-497b-b43e-8ab5d5a0d214
  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: qlora
base_model: unsloth/SmolLM-135M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - d3e5bdd73347c26c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/d3e5bdd73347c26c_train_data.json
  type:
    field_input: system_prompt
    field_instruction: problem
    field_output: solution
    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: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: error577/c06956a3-fe98-497b-b43e-8ab5d5a0d214
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 3
mlflow_experiment_name: /tmp/d3e5bdd73347c26c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
restore_best_weights: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: 6f6675a6-cb87-49fd-824d-f8d0dfce8107
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6f6675a6-cb87-49fd-824d-f8d0dfce8107
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# c06956a3-fe98-497b-b43e-8ab5d5a0d214

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: 0.9896

## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 48
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2311        | 0.0010 | 1    | 1.7154          |
| 2.1415        | 0.0971 | 100  | 1.2307          |
| 1.9643        | 0.1941 | 200  | 1.1575          |
| 1.9656        | 0.2912 | 300  | 1.1358          |
| 1.8197        | 0.3883 | 400  | 1.1138          |
| 1.9307        | 0.4853 | 500  | 1.0955          |
| 1.7157        | 0.5824 | 600  | 1.0814          |
| 1.8345        | 0.6794 | 700  | 1.0735          |
| 1.8775        | 0.7765 | 800  | 1.0685          |
| 1.687         | 0.8736 | 900  | 1.0693          |
| 1.8377        | 0.9706 | 1000 | 1.0511          |
| 0.9808        | 1.0677 | 1100 | 1.0321          |
| 1.0324        | 1.1648 | 1200 | 1.0326          |
| 1.0425        | 1.2618 | 1300 | 1.0186          |
| 0.969         | 1.3589 | 1400 | 1.0205          |
| 0.9458        | 1.4560 | 1500 | 1.0165          |
| 0.9246        | 1.5530 | 1600 | 1.0105          |
| 0.8464        | 1.6501 | 1700 | 1.0113          |
| 0.9311        | 1.7471 | 1800 | 1.0011          |
| 0.9494        | 1.8442 | 1900 | 1.0005          |
| 0.879         | 1.9413 | 2000 | 1.0046          |
| 1.0447        | 2.0383 | 2100 | 0.9988          |
| 1.0148        | 2.1354 | 2200 | 0.9965          |
| 1.0102        | 2.2325 | 2300 | 0.9945          |
| 1.0027        | 2.3295 | 2400 | 0.9951          |
| 1.2094        | 2.4266 | 2500 | 0.9949          |
| 0.9836        | 2.5237 | 2600 | 0.9879          |
| 0.9018        | 2.6207 | 2700 | 0.9893          |
| 1.0633        | 2.7178 | 2800 | 0.9944          |
| 0.9116        | 2.8149 | 2900 | 0.9896          |


### Framework versions

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