---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a30c98f1-5377-4fe4-bf1b-66b3d754b9b2
  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: Qwen/Qwen1.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 08def8d176b3ee4d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/08def8d176b3ee4d_train_data.json
  type:
    field_input: tools
    field_instruction: query
    field_output: answers
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/a30c98f1-5377-4fe4-bf1b-66b3d754b9b2
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
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
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4224
micro_batch_size: 4
mlflow_experiment_name: /tmp/08def8d176b3ee4d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 7a7bc575-869c-40c2-9816-fca44c877c0b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7a7bc575-869c-40c2-9816-fca44c877c0b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# a30c98f1-5377-4fe4-bf1b-66b3d754b9b2

This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0594

## 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: 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: 2917

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9008        | 0.0007 | 1    | 0.9272          |
| 0.1566        | 0.0686 | 100  | 0.0968          |
| 0.0716        | 0.1372 | 200  | 0.0869          |
| 0.081         | 0.2057 | 300  | 0.0793          |
| 0.1297        | 0.2743 | 400  | 0.0786          |
| 0.0308        | 0.3429 | 500  | 0.0740          |
| 0.075         | 0.4115 | 600  | 0.0740          |
| 0.0821        | 0.4801 | 700  | 0.0723          |
| 0.0985        | 0.5486 | 800  | 0.0694          |
| 0.051         | 0.6172 | 900  | 0.0675          |
| 0.0472        | 0.6858 | 1000 | 0.0666          |
| 0.0509        | 0.7544 | 1100 | 0.0658          |
| 0.1134        | 0.8230 | 1200 | 0.0651          |
| 0.073         | 0.8916 | 1300 | 0.0636          |
| 0.1127        | 0.9601 | 1400 | 0.0636          |
| 0.0228        | 1.0287 | 1500 | 0.0637          |
| 0.0262        | 1.0973 | 1600 | 0.0634          |
| 0.0448        | 1.1659 | 1700 | 0.0629          |
| 0.037         | 1.2345 | 1800 | 0.0625          |
| 0.1208        | 1.3030 | 1900 | 0.0620          |
| 0.0368        | 1.3716 | 2000 | 0.0619          |
| 0.0951        | 1.4402 | 2100 | 0.0612          |
| 0.0582        | 1.5088 | 2200 | 0.0604          |
| 0.0438        | 1.5774 | 2300 | 0.0602          |
| 0.033         | 1.6459 | 2400 | 0.0598          |
| 0.0626        | 1.7145 | 2500 | 0.0597          |
| 0.0106        | 1.7831 | 2600 | 0.0597          |
| 0.0312        | 1.8517 | 2700 | 0.0594          |
| 0.0382        | 1.9203 | 2800 | 0.0595          |
| 0.0622        | 1.9889 | 2900 | 0.0594          |


### Framework versions

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