---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_5e-5_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6606986899563319
library_name: peft
---

<!-- 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. -->

# lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_5e-5_lora2

This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2235
- Accuracy: 0.6607

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4675        | 1.0   | 250  | 1.5235          | 0.6752   |
| 1.435         | 2.0   | 500  | 1.5122          | 0.6762   |
| 1.395         | 3.0   | 750  | 1.5092          | 0.6761   |
| 1.35          | 4.0   | 1000 | 1.5165          | 0.6761   |
| 1.2906        | 5.0   | 1250 | 1.5309          | 0.6754   |
| 1.2411        | 6.0   | 1500 | 1.5509          | 0.6747   |
| 1.1833        | 7.0   | 1750 | 1.5747          | 0.6737   |
| 1.1198        | 8.0   | 2000 | 1.6129          | 0.6727   |
| 1.0498        | 9.0   | 2250 | 1.6407          | 0.6717   |
| 1.0063        | 10.0  | 2500 | 1.6802          | 0.6706   |
| 0.943         | 11.0  | 2750 | 1.7385          | 0.6691   |
| 0.8881        | 12.0  | 3000 | 1.7767          | 0.6681   |
| 0.8176        | 13.0  | 3250 | 1.8362          | 0.6669   |
| 0.7669        | 14.0  | 3500 | 1.8820          | 0.6659   |
| 0.7119        | 15.0  | 3750 | 1.9359          | 0.6648   |
| 0.6564        | 16.0  | 4000 | 2.0029          | 0.6638   |
| 0.6096        | 17.0  | 4250 | 2.0593          | 0.6631   |
| 0.5715        | 18.0  | 4500 | 2.1331          | 0.6621   |
| 0.5293        | 19.0  | 4750 | 2.1593          | 0.6617   |
| 0.4956        | 20.0  | 5000 | 2.2235          | 0.6607   |


### Framework versions

- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1