metadata

license: cc-by-nc-4.0
library_name: transformers
tags:
  - trl
  - dpo
  - conversational
language:
  - nl
datasets:
  - BramVanroy/ultra_feedback_dutch_cleaned
pipeline_tag: text-generation
inference: false

Qwen1.5-7B-Dutch-Chat

Model description

This DPO aligned model is the merged version of the adapter model robinsmits/Qwen1.5-7B-Dutch-Chat-Dpo.

DPO Finetuning was performed on the Dutch BramVanroy/ultra_feedback_dutch_cleaned dataset.

See Qwen/Qwen1.5-7B-Chat for all information about the base model.

Model usage

A basic example of how to use the finetuned model.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = 'cuda'
model_name = 'robinsmits/Qwen1.5-7B-Dutch-Chat'

model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             device_map = "auto", 
                                             torch_dtype = torch.bfloat16)

tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Hoi hoe gaat het ermee? Wat kun je me vertellen over appels?"}]

encoded_ids = tokenizer.apply_chat_template(messages, 
                                            add_generation_prompt = True,
                                            return_tensors = "pt")

generated_ids = model.generate(input_ids = encoded_ids.to(device), 
                               max_new_tokens = 256,
                               do_sample = True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Below the chat template with the generated output.

<|im_start|>system
Je bent een behulpzame AI assistent<|im_end|>
<|im_start|>user
Hoi hoe gaat het ermee? Wat kun je me vertellen over appels?<|im_end|>
<|im_start|>assistant
Hallo! Appels zijn zo'n lekkere fruitsoort. Ze zijn zoet en knapperig, en je kunt ze koken, roosteren of zelfs in smoothies doen. Er zijn heel veel verschillende soorten appels, zoals de Fuji, Granny Smith en Gala. De appels die je meestal in de winkel koopt, komen van bomen die in het oosten van Noord-Amerika groeien.<|im_end|>

Intended uses & limitations

More information needed

Training and evaluation data

The training notebook is available at the following link: Qwen1_5_7B_Dutch_Chat_DPO

It achieves the following results on the evaluation set:

Loss: 0.2610
Rewards/chosen: -0.7248
Rewards/rejected: -2.6224
Rewards/accuracies: 0.9170
Rewards/margins: 1.8976
Logps/rejected: -877.8102
Logps/chosen: -783.4282
Logits/rejected: -0.8110
Logits/chosen: -0.7528

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
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
0.5503	0.1	30	0.4684	-0.0439	-0.6295	0.8919	0.5856	-837.9513	-769.8103	-0.9335	-0.8894
0.4178	0.2	60	0.3568	-0.3713	-1.4769	0.9015	1.1056	-854.9000	-776.3594	-0.8768	-0.8276
0.3264	0.29	90	0.3143	-0.4893	-1.8730	0.9151	1.3837	-862.8228	-778.7191	-0.8428	-0.7929
0.2999	0.39	120	0.2885	-0.6832	-2.3118	0.9151	1.6286	-871.5981	-782.5971	-0.8260	-0.7730
0.3454	0.49	150	0.2749	-0.7239	-2.4904	0.9189	1.7664	-875.1693	-783.4113	-0.8235	-0.7678
0.3354	0.59	180	0.2685	-0.6775	-2.4859	0.9170	1.8084	-875.0795	-782.4824	-0.8130	-0.7574
0.2848	0.68	210	0.2652	-0.7157	-2.5692	0.9131	1.8535	-876.7465	-783.2466	-0.8157	-0.7586
0.3437	0.78	240	0.2621	-0.7233	-2.6091	0.9151	1.8857	-877.5430	-783.3994	-0.8138	-0.7561
0.2655	0.88	270	0.2611	-0.7183	-2.6154	0.9151	1.8971	-877.6708	-783.2995	-0.8106	-0.7524
0.3442	0.98	300	0.2610	-0.7248	-2.6224	0.9170	1.8976	-877.8102	-783.4282	-0.8110	-0.7528

Framework versions

PEFT 0.9.0
Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.17.1
Tokenizers 0.15.2