06072000

This model is a fine-tuned version of Qwen/Qwen2-7B-Instruct on the alpaca_formatted_ift_eft_dft_rft_share5k_2048 dataset. It achieves the following results on the evaluation set:

Loss: 0.8293

Model description

Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model.

Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.

Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts.

For more details, please refer to our blog, GitHub, and Documentation.

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 700
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7753	0.9586	700	0.8477
0.7738	1.9172	1400	0.8355
0.5842	2.8757	2100	0.8306
0.9188	3.8343	2800	0.8303
0.8923	4.7929	3500	0.8290

Framework versions

PEFT 0.10.0
Transformers 4.40.0
Pytorch 2.1.0+cu121
Datasets 2.14.5
Tokenizers 0.19.1

WDong
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lora_06072000