metadata

license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
  - accuracy
model-index:
  - name: >-
      lmind_nq_train6000_eval6489_v1_doc_qa_v3_meta-llama_Llama-2-7b-hf_3e-5_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
          type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5965641025641025

lmind_nq_train6000_eval6489_v1_doc_qa_v3_meta-llama_Llama-2-7b-hf_3e-5_lora2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3 dataset. It achieves the following results on the evaluation set:

Loss: 2.0825
Accuracy: 0.5966

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: 3e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
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: constant
lr_scheduler_warmup_ratio: 0.05
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3948	1.0	529	1.3087	0.6132
1.3789	2.0	1058	1.2897	0.6146
1.3259	3.0	1587	1.2849	0.6179
1.2853	4.0	2116	1.3169	0.6159
1.2556	5.0	2645	1.3532	0.6132
1.1972	6.0	3174	1.4135	0.6126
1.1839	7.0	3703	1.5007	0.6081
1.1334	8.0	4232	1.5242	0.6074
1.0966	9.0	4761	1.6107	0.5803
1.0485	10.0	5290	1.6749	0.6049
1.021	11.0	5819	1.7324	0.6015
0.9918	12.0	6348	1.7632	0.6007
0.947	13.0	6877	1.8303	0.6011
0.9376	14.0	7406	1.8873	0.5991
0.898	15.0	7935	1.9688	0.5976
0.8559	16.0	8464	1.9724	0.5988
0.8348	17.0	8993	1.9815	0.5714
0.8106	18.0	9522	2.0386	0.598
0.7848	19.0	10051	2.0627	0.5964
0.745	20.0	10580	2.0825	0.5966

Framework versions

Transformers 4.34.0
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.14.1