Instructions to use seeledu/Chinese-Llama-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seeledu/Chinese-Llama-2-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="seeledu/Chinese-Llama-2-7B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seeledu/Chinese-Llama-2-7B") model = AutoModel.from_pretrained("seeledu/Chinese-Llama-2-7B") - Notebooks
- Google Colab
- Kaggle
llama2-7b-llama2_coig_dt_ca-all
This model is a fine-tuned version of llama2-7B-HF on an Chinese instruction dataset.
More information can be found in this Github Homepage.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
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