Instructions to use Liu-Xiang/sql-code-llama-alan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Liu-Xiang/sql-code-llama-alan with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "Liu-Xiang/sql-code-llama-alan") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Liu-Xiang/sql-code-llama-alan: direct link, hf CLI and curl.
- Browser
- Download file 2.94 kB
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https://huggingface.co/Liu-Xiang/sql-code-llama-alan/resolve/main/README.md
- Command line
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hf download hf://Liu-Xiang/sql-code-llama-alan/README.md
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curl -L -o README.md https://huggingface.co/Liu-Xiang/sql-code-llama-alan/resolve/main/README.md
2.94 kB
| license: llama2 | |
| base_model: codellama/CodeLlama-7b-hf | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: sql-code-llama-alan | |
| results: [] | |
| library_name: peft | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # sql-code-llama-alan | |
| This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4576 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - quant_method: bitsandbytes | |
| - _load_in_8bit: True | |
| - _load_in_4bit: False | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: fp4 | |
| - bnb_4bit_use_double_quant: False | |
| - bnb_4bit_compute_dtype: float32 | |
| - bnb_4bit_quant_storage: uint8 | |
| - load_in_4bit: False | |
| - load_in_8bit: True | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0003 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 100 | |
| - training_steps: 400 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.1992 | 0.0465 | 20 | 2.0335 | | |
| | 1.14 | 0.0931 | 40 | 0.8371 | | |
| | 0.8045 | 0.1396 | 60 | 0.6549 | | |
| | 0.584 | 0.1862 | 80 | 0.5715 | | |
| | 0.3807 | 0.2327 | 100 | 0.5561 | | |
| | 0.5723 | 0.2792 | 120 | 0.5147 | | |
| | 0.4262 | 0.3258 | 140 | 0.5056 | | |
| | 0.6375 | 0.3723 | 160 | 0.5191 | | |
| | 0.4839 | 0.4188 | 180 | 0.4865 | | |
| | 0.3596 | 0.4654 | 200 | 0.4994 | | |
| | 0.5285 | 0.5119 | 220 | 0.4803 | | |
| | 0.4035 | 0.5585 | 240 | 0.4753 | | |
| | 0.6019 | 0.6050 | 260 | 0.4772 | | |
| | 0.4663 | 0.6515 | 280 | 0.4670 | | |
| | 0.345 | 0.6981 | 300 | 0.4746 | | |
| | 0.509 | 0.7446 | 320 | 0.4652 | | |
| | 0.3946 | 0.7912 | 340 | 0.4614 | | |
| | 0.5714 | 0.8377 | 360 | 0.4614 | | |
| | 0.4525 | 0.8842 | 380 | 0.4585 | | |
| | 0.3432 | 0.9308 | 400 | 0.4576 | | |
| ### Framework versions | |
| - PEFT 0.6.0.dev0 | |
| - Transformers 4.44.0.dev0 | |
| - Pytorch 2.2.2+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 | |