---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_3e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.49165079365079367
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_qa_Qwen_Qwen1.5-4B_3e-4_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_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6823
- Accuracy: 0.4917

## 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: 0.0003
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.248         | 1.0   | 250  | 2.3110          | 0.5173   |
| 1.9103        | 2.0   | 500  | 2.3740          | 0.5157   |
| 1.4896        | 3.0   | 750  | 2.5266          | 0.5112   |
| 1.109         | 4.0   | 1000 | 2.7830          | 0.5037   |
| 0.7757        | 5.0   | 1250 | 3.0311          | 0.4987   |
| 0.5994        | 6.0   | 1500 | 3.2256          | 0.4979   |
| 0.4921        | 7.0   | 1750 | 3.3517          | 0.4958   |
| 0.4575        | 8.0   | 2000 | 3.4321          | 0.4946   |
| 0.4233        | 9.0   | 2250 | 3.5151          | 0.4961   |
| 0.4178        | 10.0  | 2500 | 3.5280          | 0.4950   |
| 0.3987        | 11.0  | 2750 | 3.5547          | 0.4951   |
| 0.4033        | 12.0  | 3000 | 3.5601          | 0.4954   |
| 0.3932        | 13.0  | 3250 | 3.5859          | 0.4932   |
| 0.4012        | 14.0  | 3500 | 3.5944          | 0.4927   |
| 0.3895        | 15.0  | 3750 | 3.6038          | 0.4939   |
| 0.396         | 16.0  | 4000 | 3.6504          | 0.4932   |
| 0.3847        | 17.0  | 4250 | 3.6602          | 0.4912   |
| 0.3942        | 18.0  | 4500 | 3.6515          | 0.4914   |
| 0.3809        | 19.0  | 4750 | 3.7304          | 0.4923   |
| 0.3805        | 20.0  | 5000 | 3.6823          | 0.4917   |


### Framework versions

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