File size: 4,339 Bytes

---

library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: a7706e92-133c-4e5d-bca1-aad5a4fc27e6
  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

accelerate_config:

  dynamo_backend: inductor

  mixed_precision: bf16

  num_machines: 1

  num_processes: auto

  use_cpu: false

adapter: lora

base_model: Qwen/Qwen2.5-1.5B-Instruct

bf16: auto

chat_template: llama3

dataset_prepared_path: null

datasets:

- data_files:

  - 733c43e45c9d282a_train_data.json

  ds_type: json

  format: custom

  path: /workspace/input_data/733c43e45c9d282a_train_data.json

  type:

    field_instruction: problem

    field_output: solution

    format: '{instruction}'

    no_input_format: '{instruction}'

    system_format: '{system}'

    system_prompt: ''

debug: null

deepspeed: null

device_map: auto

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: 16

gradient_checkpointing: true

group_by_length: false

hub_model_id: VERSIL91/a7706e92-133c-4e5d-bca1-aad5a4fc27e6

hub_repo: null

hub_strategy: checkpoint

hub_token: null

learning_rate: 0.0001

local_rank: null

logging_steps: 1

lora_alpha: 16

lora_dropout: 0.05

lora_fan_in_fan_out: null

lora_model_dir: null

lora_r: 8

lora_target_linear: true

lora_target_modules:

- q_proj

- v_proj

lr_scheduler: cosine

max_memory:

  0: 70GiB

max_steps: 50

micro_batch_size: 2

mlflow_experiment_name: /tmp/733c43e45c9d282a_train_data.json

model_type: AutoModelForCausalLM

num_epochs: 1

optimizer: adamw_bnb_8bit

output_dir: miner_id_24

pad_to_sequence_len: true

quantization_config:

  llm_int8_enable_fp32_cpu_offload: true

  load_in_8bit: true

resume_from_checkpoint: null

s2_attention: null

sample_packing: false

saves_per_epoch: 4

sequence_len: 512

strict: false

tf32: false

tokenizer_type: AutoTokenizer

torch_compile: true

train_on_inputs: false

trust_remote_code: true

val_set_size: 0.05

wandb_entity: null

wandb_mode: online

wandb_name: a7706e92-133c-4e5d-bca1-aad5a4fc27e6

wandb_project: Gradients-On-Demand

wandb_run: your_name

wandb_runid: a7706e92-133c-4e5d-bca1-aad5a4fc27e6

warmup_steps: 10

weight_decay: 0.0

xformers_attention: null



```

</details><br>

# a7706e92-133c-4e5d-bca1-aad5a4fc27e6

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5173

## 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: 2

- eval_batch_size: 2

- seed: 42

- gradient_accumulation_steps: 16

- 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: 50

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6358        | 0.0001 | 1    | 0.6944          |
| 0.5682        | 0.0019 | 13   | 0.6041          |
| 0.5173        | 0.0039 | 26   | 0.5379          |
| 0.5272        | 0.0058 | 39   | 0.5173          |


### Framework versions

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