---
library_name: peft
base_model: katuni4ka/tiny-random-qwen1.5-moe
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f46cc6fc-f320-4e60-88e9-6f617fd7b16a
  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: katuni4ka/tiny-random-qwen1.5-moe
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 22c6d07080dfffd0_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/22c6d07080dfffd0_train_data.json
  type:
    field_instruction: text
    field_output: label_text
    format: '{instruction}'
    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: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: leixa/f46cc6fc-f320-4e60-88e9-6f617fd7b16a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/22c6d07080dfffd0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: f46cc6fc-f320-4e60-88e9-6f617fd7b16a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f46cc6fc-f320-4e60-88e9-6f617fd7b16a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# f46cc6fc-f320-4e60-88e9-6f617fd7b16a

This model is a fine-tuned version of [katuni4ka/tiny-random-qwen1.5-moe](https://huggingface.co/katuni4ka/tiny-random-qwen1.5-moe) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.4434

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0005 | 1    | 11.9442         |
| 11.642        | 0.0189 | 42   | 11.6242         |
| 11.4712       | 0.0378 | 84   | 11.4749         |
| 11.4696       | 0.0567 | 126  | 11.4634         |
| 11.4574       | 0.0757 | 168  | 11.4591         |
| 11.4622       | 0.0946 | 210  | 11.4558         |
| 11.4469       | 0.1135 | 252  | 11.4504         |
| 11.4435       | 0.1324 | 294  | 11.4483         |
| 11.4324       | 0.1513 | 336  | 11.4461         |
| 11.4376       | 0.1702 | 378  | 11.4444         |
| 11.441        | 0.1891 | 420  | 11.4436         |
| 11.4425       | 0.2080 | 462  | 11.4434         |


### Framework versions

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