---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
      type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7918744394618834
library_name: peft
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-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_nq_train6000_eval6489_v1_recite_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4565
- Accuracy: 0.7919

## 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: 0.0005
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7331        | 1.0   | 529   | 1.4271          | 0.6365   |
| 1.3037        | 2.0   | 1058  | 1.0687          | 0.6846   |
| 0.8818        | 3.0   | 1587  | 0.8142          | 0.7216   |
| 0.6397        | 4.0   | 2116  | 0.6636          | 0.7470   |
| 0.4735        | 5.0   | 2645  | 0.5547          | 0.7667   |
| 0.3798        | 6.0   | 3174  | 0.5002          | 0.7764   |
| 0.3409        | 7.0   | 3703  | 0.4850          | 0.7801   |
| 0.3054        | 8.0   | 4232  | 0.4691          | 0.7835   |
| 0.2803        | 9.0   | 4761  | 0.4637          | 0.7859   |
| 0.2637        | 10.0  | 5290  | 0.4532          | 0.7877   |
| 0.2661        | 11.0  | 5819  | 0.4668          | 0.7879   |
| 0.2513        | 12.0  | 6348  | 0.4647          | 0.7893   |
| 0.2424        | 13.0  | 6877  | 0.4615          | 0.7897   |
| 0.2499        | 14.0  | 7406  | 0.4546          | 0.7894   |
| 0.235         | 15.0  | 7935  | 0.4668          | 0.7896   |
| 0.2317        | 16.0  | 8464  | 0.4510          | 0.7913   |
| 0.2225        | 17.0  | 8993  | 0.4497          | 0.7915   |
| 0.2358        | 18.0  | 9522  | 0.4475          | 0.7916   |
| 0.2253        | 19.0  | 10051 | 0.4529          | 0.7918   |
| 0.2172        | 20.0  | 10580 | 0.4565          | 0.7919   |


### Framework versions

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