README.md

---

library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: 9d122ead-3ae3-4958-85c8-6e51f0ea6d73
  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/Qwen2.5-3B-Instruct

bf16: auto

chat_template: llama3

dataset_prepared_path: null

datasets:

- data_files:

  - 60a7588018415c1f_train_data.json

  ds_type: json

  format: custom

  path: /workspace/input_data/60a7588018415c1f_train_data.json

  type:

    field_instruction: prompt

    field_output: ground_truth_chosen

    format: '{instruction}'

    no_input_format: '{instruction}'

    system_format: '{system}'

    system_prompt: ''

debug: null

deepspeed: null

early_stopping_patience: 1

eval_max_new_tokens: 128

eval_steps: 25

eval_table_size: null

flash_attention: false

fp16: false

fsdp: null

fsdp_config: null

gradient_accumulation_steps: 4

gradient_checkpointing: true

group_by_length: true

hub_model_id: Dnsx077/9d122ead-3ae3-4958-85c8-6e51f0ea6d73

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: 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: 70GB

max_steps: 50

micro_batch_size: 2

mlflow_experiment_name: /tmp/60a7588018415c1f_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

sequence_len: 4056

strict: false

tf32: false

tokenizer_type: AutoTokenizer

train_on_inputs: false

trust_remote_code: true

val_set_size: 0.05

wandb_entity: taoxminer-education

wandb_mode: online

wandb_name: 9d122ead-3ae3-4958-85c8-6e51f0ea6d73

wandb_project: Gradients-On-Demand

wandb_run: taoxminer

wandb_runid: 9d122ead-3ae3-4958-85c8-6e51f0ea6d73

warmup_ratio: 0.05

weight_decay: 0.01

xformers_attention: true



```

</details><br>

# 9d122ead-3ae3-4958-85c8-6e51f0ea6d73

This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1243

## 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: 4

- total_train_batch_size: 8
- 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: 2

- training_steps: 50

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6172        | 0.0003 | 1    | 1.9064          |
| 1.3437        | 0.0067 | 25   | 1.1470          |
| 1.7072        | 0.0133 | 50   | 1.1243          |


### Framework versions

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