---
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_3e-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: 0.7981434977578475
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_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_nq_train6000_eval6489_v1_recite_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4311
- Accuracy: 0.7981

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7637        | 1.0   | 529   | 1.4995          | 0.6288   |
| 1.3986        | 2.0   | 1058  | 1.1711          | 0.6720   |
| 0.9515        | 3.0   | 1587  | 0.8766          | 0.7148   |
| 0.642         | 4.0   | 2116  | 0.6720          | 0.7478   |
| 0.4362        | 5.0   | 2645  | 0.5458          | 0.7697   |
| 0.3201        | 6.0   | 3174  | 0.4751          | 0.7823   |
| 0.2652        | 7.0   | 3703  | 0.4510          | 0.7887   |
| 0.2263        | 8.0   | 4232  | 0.4372          | 0.7914   |
| 0.2035        | 9.0   | 4761  | 0.4335          | 0.7940   |
| 0.1913        | 10.0  | 5290  | 0.4322          | 0.7950   |
| 0.188         | 11.0  | 5819  | 0.4379          | 0.7945   |
| 0.1777        | 12.0  | 6348  | 0.4279          | 0.7957   |
| 0.1723        | 13.0  | 6877  | 0.4326          | 0.7956   |
| 0.1767        | 14.0  | 7406  | 0.4329          | 0.7967   |
| 0.1666        | 15.0  | 7935  | 0.4396          | 0.7962   |
| 0.1642        | 16.0  | 8464  | 0.4391          | 0.7965   |
| 0.1575        | 17.0  | 8993  | 0.4405          | 0.7967   |
| 0.1634        | 18.0  | 9522  | 0.4265          | 0.7976   |
| 0.1593        | 19.0  | 10051 | 0.4323          | 0.7978   |
| 0.153         | 20.0  | 10580 | 0.4311          | 0.7981   |


### Framework versions

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