---
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_3e-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: 0.6657583697234353
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_3e-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: 1.8696
- Accuracy: 0.6658

## 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: 3e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4742        | 1.0   | 250  | 1.5313          | 0.6748   |
| 1.45          | 2.0   | 500  | 1.5196          | 0.6757   |
| 1.4269        | 3.0   | 750  | 1.5134          | 0.6761   |
| 1.3999        | 4.0   | 1000 | 1.5120          | 0.6762   |
| 1.3614        | 5.0   | 1250 | 1.5192          | 0.6760   |
| 1.3303        | 6.0   | 1500 | 1.5266          | 0.6755   |
| 1.2946        | 7.0   | 1750 | 1.5446          | 0.6747   |
| 1.2518        | 8.0   | 2000 | 1.5590          | 0.6745   |
| 1.2082        | 9.0   | 2250 | 1.5717          | 0.6740   |
| 1.19          | 10.0  | 2500 | 1.6022          | 0.6727   |
| 1.1523        | 11.0  | 2750 | 1.6098          | 0.6726   |
| 1.1193        | 12.0  | 3000 | 1.6345          | 0.6716   |
| 1.0736        | 13.0  | 3250 | 1.6748          | 0.6707   |
| 1.0414        | 14.0  | 3500 | 1.6880          | 0.6701   |
| 1.0069        | 15.0  | 3750 | 1.7182          | 0.6694   |
| 0.9654        | 16.0  | 4000 | 1.7522          | 0.6685   |
| 0.9337        | 17.0  | 4250 | 1.7826          | 0.6677   |
| 0.9           | 18.0  | 4500 | 1.8080          | 0.6672   |
| 0.8704        | 19.0  | 4750 | 1.8350          | 0.6663   |
| 0.8407        | 20.0  | 5000 | 1.8696          | 0.6658   |


### Framework versions

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