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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- axolotl |
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- generated_from_trainer |
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base_model: mistralai/Mistral-7B-v0.1 |
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model-index: |
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- name: NistCodeLlama-7b |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: mistralai/Mistral-7B-v0.1 |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_llama_derived_model: true |
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hub_model_id: NistCodeLlama-7b |
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sample_packing: false |
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eval_sample_packing: false |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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datasets: |
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- path: rkreddyp/nist_800_53 |
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ds_type: json |
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type: |
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field_instruction: question |
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field_input: context |
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field_output: answer |
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format: |- |
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[INST] Using the schema context below, generate a SQL query that answers the question. |
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{input} |
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{instruction} [/INST] |
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dataset_prepared_path: |
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val_set_size: 0.02 |
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output_dir: ./qlora-out |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 2048 |
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sample_packing: true |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: axolotl-nist |
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wandb_entity: |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: paged_adamw_32bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 100 |
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eval_steps: 0.01 |
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save_strategy: epoch |
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save_steps: |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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</details><br> |
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# NistCodeLlama-7b |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3414 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.4855 | 0.06 | 1 | 1.4808 | |
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| 1.4522 | 0.11 | 2 | 1.4811 | |
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| 1.4616 | 0.17 | 3 | 1.4788 | |
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| 1.5276 | 0.23 | 4 | 1.4746 | |
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| 1.4564 | 0.29 | 5 | 1.4662 | |
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| 1.4837 | 0.34 | 6 | 1.4515 | |
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| 1.4709 | 0.4 | 7 | 1.4280 | |
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| 1.3571 | 0.46 | 8 | 1.3903 | |
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| 1.4164 | 0.51 | 9 | 1.3363 | |
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| 1.3257 | 0.57 | 10 | 1.2692 | |
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| 1.2858 | 0.63 | 11 | 1.2027 | |
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| 1.2318 | 0.69 | 12 | 1.1364 | |
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| 1.1164 | 0.74 | 13 | 1.0595 | |
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| 1.0984 | 0.8 | 14 | 0.9748 | |
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| 0.9593 | 0.86 | 15 | 0.8923 | |
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| 0.8325 | 0.91 | 16 | 0.8137 | |
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| 0.8357 | 0.97 | 17 | 0.7426 | |
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| 0.6483 | 1.03 | 18 | 0.6868 | |
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| 0.7138 | 1.06 | 19 | 0.6400 | |
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| 0.6105 | 1.11 | 20 | 0.6027 | |
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| 0.6409 | 1.17 | 21 | 0.5686 | |
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| 0.5206 | 1.23 | 22 | 0.5317 | |
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| 0.521 | 1.29 | 23 | 0.4962 | |
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| 0.4409 | 1.34 | 24 | 0.4697 | |
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| 0.4678 | 1.4 | 25 | 0.4481 | |
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| 0.3731 | 1.46 | 26 | 0.4303 | |
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| 0.388 | 1.51 | 27 | 0.4161 | |
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| 0.3463 | 1.57 | 28 | 0.4085 | |
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| 0.3699 | 1.63 | 29 | 0.4035 | |
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| 0.3673 | 1.69 | 30 | 0.3992 | |
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| 0.4485 | 1.74 | 31 | 0.3962 | |
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| 0.3855 | 1.8 | 32 | 0.3929 | |
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| 0.3249 | 1.86 | 33 | 0.3887 | |
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| 0.3528 | 1.91 | 34 | 0.3839 | |
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| 0.372 | 1.97 | 35 | 0.3801 | |
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| 0.3922 | 2.03 | 36 | 0.3768 | |
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| 0.3783 | 2.06 | 37 | 0.3739 | |
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| 0.31 | 2.11 | 38 | 0.3721 | |
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| 0.275 | 2.17 | 39 | 0.3699 | |
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| 0.338 | 2.23 | 40 | 0.3665 | |
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| 0.3238 | 2.29 | 41 | 0.3633 | |
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| 0.3382 | 2.34 | 42 | 0.3597 | |
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| 0.3467 | 2.4 | 43 | 0.3567 | |
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| 0.3494 | 2.46 | 44 | 0.3541 | |
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| 0.3431 | 2.51 | 45 | 0.3533 | |
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| 0.3433 | 2.57 | 46 | 0.3522 | |
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| 0.304 | 2.63 | 47 | 0.3491 | |
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| 0.3098 | 2.69 | 48 | 0.3464 | |
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| 0.279 | 2.74 | 49 | 0.3443 | |
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| 0.3105 | 2.8 | 50 | 0.3425 | |
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| 0.2305 | 2.86 | 51 | 0.3414 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.0 |
