---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-1.5B-Instruct
- llama-factory
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: sft_v2_61w
  results: []
---

<!-- 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. -->

本项工作在同元软控实习期间完成，旨在通过微调得到更适配 Julia 语言的大模型。

# sft_v2_61w

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on the julia_func_datasetv2_61w dataset.
It achieves the following results on the evaluation set:

- Loss: 0.0347

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0393        | 0.6621 | 3000 | 0.0376          |
| 0.031         | 1.3240 | 6000 | 0.0356          |
| 0.0297        | 1.9860 | 9000 | 0.0346          |


### Framework versions

- PEFT 0.17.1
- Transformers 4.56.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.1