End of training

README.md

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+---
+license: llama2
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: codellama/CodeLlama-13b-Instruct-hf
+model-index:
+- name: stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
+  results: []
+---
+
+<!-- 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. -->
+
+# stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
+
+This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0472
+
+## 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: 0.0002
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 0.3435        | 0.03  | 100  | 0.0703          |
+| 0.0654        | 0.07  | 200  | 0.0586          |
+| 0.0579        | 0.1   | 300  | 0.0563          |
+| 0.0567        | 0.14  | 400  | 0.0562          |
+| 0.0551        | 0.17  | 500  | 0.0547          |
+| 0.0547        | 0.21  | 600  | 0.0526          |
+| 0.0532        | 0.24  | 700  | 0.0516          |
+| 0.0534        | 0.28  | 800  | 0.0515          |
+| 0.0521        | 0.31  | 900  | 0.0520          |
+| 0.0522        | 0.35  | 1000 | 0.0517          |
+| 0.0518        | 0.38  | 1100 | 0.0511          |
+| 0.051         | 0.42  | 1200 | 0.0502          |
+| 0.0517        | 0.45  | 1300 | 0.0494          |
+| 0.0506        | 0.49  | 1400 | 0.0499          |
+| 0.0511        | 0.52  | 1500 | 0.0496          |
+| 0.05          | 0.56  | 1600 | 0.0493          |
+| 0.05          | 0.59  | 1700 | 0.0497          |
+| 0.049         | 0.63  | 1800 | 0.0485          |
+| 0.0487        | 0.66  | 1900 | 0.0484          |
+| 0.0492        | 0.7   | 2000 | 0.0483          |
+| 0.0493        | 0.73  | 2100 | 0.0481          |
+| 0.0483        | 0.77  | 2200 | 0.0478          |
+| 0.048         | 0.8   | 2300 | 0.0478          |
+| 0.048         | 0.83  | 2400 | 0.0476          |
+| 0.0476        | 0.87  | 2500 | 0.0474          |
+| 0.0471        | 0.9   | 2600 | 0.0473          |
+| 0.0472        | 0.94  | 2700 | 0.0472          |
+| 0.0469        | 0.97  | 2800 | 0.0472          |
+
+
+### Framework versions
+
+- Transformers 4.37.0.dev0
+- Pytorch 2.1.2+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0
+## Training procedure
+
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: QuantizationMethod.BITS_AND_BYTES
+- load_in_8bit: False
+- load_in_4bit: True
+- 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: nf4
+- bnb_4bit_use_double_quant: True
+- bnb_4bit_compute_dtype: bfloat16
+
+### Framework versions
+
+
+- PEFT 0.6.2

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