Instructions to use adamo1139/Experimental-DeepSeek-V2-Coder-Lite-JUMP-alpha1-LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adamo1139/Experimental-DeepSeek-V2-Coder-Lite-JUMP-alpha1-LORA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/media/adamo/usemka/maked/LLM_Models/DeepSeek-Coder-V2-Lite-Base") model = PeftModel.from_pretrained(base_model, "adamo1139/Experimental-DeepSeek-V2-Coder-Lite-JUMP-alpha1-LORA") - Notebooks
- Google Colab
- Kaggle
train_2024-09-01-02-35-42qw
This model is a fine-tuned version of /media/adamo/usemka/maked/LLM_Models/DeepSeek-Coder-V2-Lite-Base on the adamo1139/ise-uiuc_Magicoder-Evol-Instruct-110K-ShareGPT dataset.
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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 0.25
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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