tanliboy_-_lambda-gemma-2-9b-dpo-gguf

RichardErkhov/tanliboy_-_lambda-gemma-2-9b-dpo-gguf

tanliboy_-_lambda-gemma-2-9b-dpo-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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At a glance

Access: Public
Created: Oct 12, 2024
Updated: Oct 12, 2024
SHA: 199eafce

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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lambda-gemma-2-9b-dpo - GGUF

Model creator: https://huggingface.co/tanliboy/
Original model: https://huggingface.co/tanliboy/lambda-gemma-2-9b-dpo/

Name	Quant method	Size
lambda-gemma-2-9b-dpo.Q2_K.gguf	Q2_K	3.54GB
lambda-gemma-2-9b-dpo.IQ3_XS.gguf	IQ3_XS	3.86GB
lambda-gemma-2-9b-dpo.IQ3_S.gguf	IQ3_S	4.04GB
lambda-gemma-2-9b-dpo.Q3_K_S.gguf	Q3_K_S	4.04GB
lambda-gemma-2-9b-dpo.IQ3_M.gguf	IQ3_M	4.19GB
lambda-gemma-2-9b-dpo.Q3_K.gguf	Q3_K	4.43GB
lambda-gemma-2-9b-dpo.Q3_K_M.gguf	Q3_K_M	4.43GB
lambda-gemma-2-9b-dpo.Q3_K_L.gguf	Q3_K_L	4.78GB
lambda-gemma-2-9b-dpo.IQ4_XS.gguf	IQ4_XS	4.86GB
lambda-gemma-2-9b-dpo.Q4_0.gguf	Q4_0	5.07GB
lambda-gemma-2-9b-dpo.IQ4_NL.gguf	IQ4_NL	5.1GB
lambda-gemma-2-9b-dpo.Q4_K_S.gguf	Q4_K_S	5.1GB
lambda-gemma-2-9b-dpo.Q4_K.gguf	Q4_K	5.37GB
lambda-gemma-2-9b-dpo.Q4_K_M.gguf	Q4_K_M	5.37GB
lambda-gemma-2-9b-dpo.Q4_1.gguf	Q4_1	5.55GB
lambda-gemma-2-9b-dpo.Q5_0.gguf	Q5_0	6.04GB
lambda-gemma-2-9b-dpo.Q5_K_S.gguf	Q5_K_S	6.04GB
lambda-gemma-2-9b-dpo.Q5_K.gguf	Q5_K	6.19GB
lambda-gemma-2-9b-dpo.Q5_K_M.gguf	Q5_K_M	6.19GB
lambda-gemma-2-9b-dpo.Q5_1.gguf	Q5_1	6.52GB
lambda-gemma-2-9b-dpo.Q6_K.gguf	Q6_K	7.07GB
lambda-gemma-2-9b-dpo.Q8_0.gguf	Q8_0	9.15GB

Original model description:

license: gemma base_model: tanliboy/zephyr-gemma-2-9b-sft tags:

alignment-handbook
trl
dpo
generated_from_trainer
trl
dpo
generated_from_trainer datasets:
HuggingFaceH4/ultrafeedback_binarized model-index:
name: zephyr-gemma-2-9b-dpo-2 results: []

zephyr-gemma-2-9b-dpo-2

This model is a fine-tuned version of tanliboy/zephyr-gemma-2-9b-sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5628
Rewards/chosen: -0.7292
Rewards/rejected: -1.2825
Rewards/accuracies: 0.6960
Rewards/margins: 0.5533
Logps/rejected: -1566.9301
Logps/chosen: -1043.5624
Logits/rejected: -14.1720
Logits/chosen: -14.6638

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: 16
total_train_batch_size: 256
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: 0.2
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
0.6835	0.2094	50	0.6815	-0.0218	-0.0436	0.6560	0.0218	-328.0053	-336.0947	-11.6381	-11.3403
0.6243	0.4187	100	0.6229	-0.5238	-0.7528	0.6600	0.2290	-1037.2136	-838.1255	-15.5098	-15.6787
0.5625	0.6281	150	0.5793	-0.7186	-1.1873	0.6880	0.4688	-1471.7362	-1032.8834	-14.7746	-15.1797
0.5699	0.8375	200	0.5647	-0.6443	-1.1499	0.6920	0.5057	-1434.3335	-958.5825	-14.1861	-14.6684

Framework versions

Transformers 4.43.1
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Created: Oct 12, 2024
Updated: Oct 12, 2024