---
base_model: slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8
datasets:
- slm-research-vn/dpo-format-function-calling-v4
- slm-research-vn/dpo-format-glaive-code-assistant-v3-with-mistral-large-slm-iter4
- argilla/dpo-mix-7k
library_name: peft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
model-index:
- name: Qwen2-7B-Instruct-SPPO-Function-call-v2.12
  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. -->

# Qwen2-7B-Instruct-SPPO-Function-call-v2.12

This model is a fine-tuned version of [slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8](https://huggingface.co/slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8) on the slm-research-vn/dpo-format-function-calling-v4, the slm-research-vn/dpo-format-glaive-code-assistant-v3-with-mistral-large-slm-iter4 and the argilla/dpo-mix-7k datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3322
- Rewards/chosen: 0.5523
- Rewards/rejected: -0.7005
- Rewards/accuracies: 0.9017
- Rewards/margins: 1.2528
- Logps/rejected: -278.7327
- Logps/chosen: -129.0717
- Logits/rejected: -0.5984
- Logits/chosen: -0.7738

## 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: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6806        | 0.0916 | 100  | 0.6816          | 0.0303         | 0.0099           | 0.6445             | 0.0205          | -264.5260      | -139.5110    | -0.5879         | -0.7638       |
| 0.5704        | 0.1832 | 200  | 0.5993          | 0.3495         | 0.1473           | 0.8237             | 0.2023          | -261.7780      | -133.1277    | -0.5881         | -0.7638       |
| 0.5032        | 0.2749 | 300  | 0.5313          | 0.5795         | 0.1792           | 0.8526             | 0.4003          | -261.1383      | -128.5271    | -0.5893         | -0.7651       |
| 0.4548        | 0.3665 | 400  | 0.4727          | 0.6406         | 0.0523           | 0.8844             | 0.5884          | -263.6780      | -127.3051    | -0.5901         | -0.7660       |
| 0.3823        | 0.4581 | 500  | 0.4235          | 0.6412         | -0.1314          | 0.8931             | 0.7726          | -267.3507      | -127.2934    | -0.5914         | -0.7672       |
| 0.3513        | 0.5497 | 600  | 0.3843          | 0.6087         | -0.3415          | 0.9133             | 0.9502          | -271.5532      | -127.9448    | -0.5936         | -0.7693       |
| 0.3444        | 0.6413 | 700  | 0.3571          | 0.5871         | -0.5028          | 0.9104             | 1.0898          | -274.7784      | -128.3763    | -0.5965         | -0.7721       |
| 0.3486        | 0.7329 | 800  | 0.3427          | 0.5681         | -0.6155          | 0.9104             | 1.1836          | -277.0341      | -128.7559    | -0.5971         | -0.7725       |
| 0.3317        | 0.8246 | 900  | 0.3349          | 0.5586         | -0.6739          | 0.9133             | 1.2326          | -278.2013      | -128.9451    | -0.5993         | -0.7748       |
| 0.3077        | 0.9162 | 1000 | 0.3328          | 0.5530         | -0.6974          | 0.9075             | 1.2504          | -278.6715      | -129.0585    | -0.5998         | -0.7754       |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1