---
base_model: slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5
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-v2.6
  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-v2.6

This model is a fine-tuned version of [slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5](https://huggingface.co./slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5) 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: 0.3005
- Rewards/chosen: 1.6737
- Rewards/rejected: -0.4932
- Rewards/accuracies: 0.8699
- Rewards/margins: 2.1670
- Logps/rejected: -276.8380
- Logps/chosen: -200.9362
- Logits/rejected: -0.6568
- Logits/chosen: -0.6408

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6437        | 0.0916 | 100  | 0.6128          | 0.3050         | 0.0739           | 0.7254             | 0.2311          | -265.4963      | -228.3116    | -0.7319         | -0.7206       |
| 0.5175        | 0.1832 | 200  | 0.4987          | 1.1265         | 0.2914           | 0.8237             | 0.8351          | -261.1460      | -211.8815    | -0.7134         | -0.7068       |
| 0.3903        | 0.2749 | 300  | 0.4279          | 1.7297         | 0.4889           | 0.8468             | 1.2408          | -257.1960      | -199.8173    | -0.6700         | -0.6642       |
| 0.3712        | 0.3665 | 400  | 0.3781          | 1.7272         | 0.2255           | 0.8468             | 1.5017          | -262.4645      | -199.8672    | -0.6756         | -0.6691       |
| 0.3064        | 0.4581 | 500  | 0.3477          | 1.7220         | -0.0183          | 0.8613             | 1.7403          | -267.3389      | -199.9704    | -0.6642         | -0.6488       |
| 0.3054        | 0.5497 | 600  | 0.3271          | 1.6469         | -0.1977          | 0.8671             | 1.8447          | -270.9281      | -201.4723    | -0.6576         | -0.6407       |
| 0.2919        | 0.6413 | 700  | 0.3144          | 1.7376         | -0.3034          | 0.8642             | 2.0410          | -273.0414      | -199.6590    | -0.6753         | -0.6672       |
| 0.314         | 0.7329 | 800  | 0.3056          | 1.7037         | -0.4229          | 0.8671             | 2.1266          | -275.4323      | -200.3379    | -0.6685         | -0.6574       |
| 0.3014        | 0.8246 | 900  | 0.3020          | 1.6807         | -0.4632          | 0.8699             | 2.1439          | -276.2374      | -200.7971    | -0.6702         | -0.6641       |
| 0.268         | 0.9162 | 1000 | 0.2999          | 1.6798         | -0.4929          | 0.8844             | 2.1726          | -276.8312      | -200.8157    | -0.6690         | -0.6635       |


### Framework versions

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