Quantization made by Richard Erkhov.

lambda-qwen2.5-14b-dpo-test - GGUF

Model creator: https://huggingface.co/tanliboy/
Original model: https://huggingface.co/tanliboy/lambda-qwen2.5-14b-dpo-test/

Name	Quant method	Size
lambda-qwen2.5-14b-dpo-test.Q2_K.gguf	Q2_K	5.37GB
lambda-qwen2.5-14b-dpo-test.IQ3_XS.gguf	IQ3_XS	5.94GB
lambda-qwen2.5-14b-dpo-test.IQ3_S.gguf	IQ3_S	6.23GB
lambda-qwen2.5-14b-dpo-test.Q3_K_S.gguf	Q3_K_S	6.2GB
lambda-qwen2.5-14b-dpo-test.IQ3_M.gguf	IQ3_M	6.44GB
lambda-qwen2.5-14b-dpo-test.Q3_K.gguf	Q3_K	6.84GB
lambda-qwen2.5-14b-dpo-test.Q3_K_M.gguf	Q3_K_M	6.84GB
lambda-qwen2.5-14b-dpo-test.Q3_K_L.gguf	Q3_K_L	7.38GB
lambda-qwen2.5-14b-dpo-test.IQ4_XS.gguf	IQ4_XS	7.62GB
lambda-qwen2.5-14b-dpo-test.Q4_0.gguf	Q4_0	7.93GB
lambda-qwen2.5-14b-dpo-test.IQ4_NL.gguf	IQ4_NL	8.01GB
lambda-qwen2.5-14b-dpo-test.Q4_K_S.gguf	Q4_K_S	7.98GB
lambda-qwen2.5-14b-dpo-test.Q4_K.gguf	Q4_K	8.37GB
lambda-qwen2.5-14b-dpo-test.Q4_K_M.gguf	Q4_K_M	8.37GB
lambda-qwen2.5-14b-dpo-test.Q4_1.gguf	Q4_1	8.75GB
lambda-qwen2.5-14b-dpo-test.Q5_0.gguf	Q5_0	9.56GB
lambda-qwen2.5-14b-dpo-test.Q5_K_S.gguf	Q5_K_S	9.56GB
lambda-qwen2.5-14b-dpo-test.Q5_K.gguf	Q5_K	9.79GB
lambda-qwen2.5-14b-dpo-test.Q5_K_M.gguf	Q5_K_M	9.79GB
lambda-qwen2.5-14b-dpo-test.Q5_1.gguf	Q5_1	10.38GB
lambda-qwen2.5-14b-dpo-test.Q6_K.gguf	Q6_K	11.29GB
lambda-qwen2.5-14b-dpo-test.Q8_0.gguf	Q8_0	14.62GB

Original model description:

library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: lambda-qwen2.5-14b-dpo-test results: []

lambda-qwen2.5-14b-dpo-test

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.4919
Rewards/chosen: -2.4745
Rewards/rejected: -3.3729
Rewards/accuracies: 0.7400
Rewards/margins: 0.8984
Logps/rejected: -832.0724
Logps/chosen: -737.5234
Logits/rejected: -1.2739
Logits/chosen: -1.2560

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: 5e-07
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
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
0.5269	0.2094	100	0.5333	-1.6756	-2.3320	0.7000	0.6564	-727.9815	-657.6356	-1.3952	-1.3850
0.5086	0.4187	200	0.5044	-2.0906	-2.9287	0.7040	0.8381	-787.6511	-699.1298	-1.2939	-1.2773
0.4787	0.6281	300	0.4948	-2.2927	-3.1689	0.7320	0.8762	-811.6696	-719.3386	-1.2846	-1.2646
0.4825	0.8375	400	0.4924	-2.4470	-3.3410	0.7400	0.8939	-828.8748	-734.7765	-1.2644	-1.2477

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1