---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5269e253-7a86-4bb0-b886-b46cbe7b732a
  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-Instruct
bf16: true
chat_template: llama3
datasets:
- data_files:
  - a761b57816c8798d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a761b57816c8798d_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output_1
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso11/5269e253-7a86-4bb0-b886-b46cbe7b732a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 77GiB
max_steps: 50
micro_batch_size: 8
mlflow_experiment_name: /tmp/a761b57816c8798d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: be00a9a0-65e6-4c81-983e-87f2acc483cb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: be00a9a0-65e6-4c81-983e-87f2acc483cb
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

```

</details><br>

# 5269e253-7a86-4bb0-b886-b46cbe7b732a

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

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 50

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0           | 0.0034 | 1    | nan             |
| 0.0           | 0.0170 | 5    | nan             |
| 0.0           | 0.0340 | 10   | nan             |
| 0.0           | 0.0509 | 15   | nan             |
| 0.0           | 0.0679 | 20   | nan             |
| 0.0           | 0.0849 | 25   | nan             |
| 0.0           | 0.1019 | 30   | nan             |
| 0.0           | 0.1188 | 35   | nan             |
| 0.0           | 0.1358 | 40   | nan             |
| 0.0           | 0.1528 | 45   | nan             |
| 0.0           | 0.1698 | 50   | nan             |


### Framework versions

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