---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_recite_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_recite_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7763580786026201
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_recite_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_recite_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4756
- Accuracy: 0.7764

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 1.5356        | 0.9998  | 1089  | 1.3711          | 0.6864   |
| 1.3102        | 1.9995  | 2178  | 1.1753          | 0.7020   |
| 1.0549        | 2.9993  | 3267  | 1.0095          | 0.7164   |
| 0.8461        | 4.0     | 4357  | 0.8722          | 0.7297   |
| 0.701         | 4.9998  | 5446  | 0.7641          | 0.7406   |
| 0.5977        | 5.9995  | 6535  | 0.6797          | 0.7490   |
| 0.5238        | 6.9993  | 7624  | 0.6209          | 0.7559   |
| 0.4742        | 8.0     | 8714  | 0.5837          | 0.7600   |
| 0.438         | 8.9998  | 9803  | 0.5532          | 0.7638   |
| 0.402         | 9.9995  | 10892 | 0.5331          | 0.7664   |
| 0.383         | 10.9993 | 11981 | 0.5156          | 0.7685   |
| 0.3627        | 12.0    | 13071 | 0.5070          | 0.7702   |
| 0.3521        | 12.9998 | 14160 | 0.4984          | 0.7714   |
| 0.344         | 13.9995 | 15249 | 0.4925          | 0.7722   |
| 0.3341        | 14.9993 | 16338 | 0.4847          | 0.7736   |
| 0.3275        | 16.0    | 17428 | 0.4808          | 0.7748   |
| 0.3223        | 16.9998 | 18517 | 0.4776          | 0.7751   |
| 0.3155        | 17.9995 | 19606 | 0.4804          | 0.7758   |
| 0.3033        | 18.9993 | 20695 | 0.4787          | 0.7761   |
| 0.2989        | 19.9954 | 21780 | 0.4756          | 0.7764   |


### Framework versions

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