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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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- generated_from_trainer |
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base_model: TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ |
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model-index: |
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- name: output_solor/exp_16 |
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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: TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ |
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is_llama_derived_model: false |
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gptq: true |
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gptq_disable_exllama: true |
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model_type: AutoModelForCausalLM |
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tokenizer_type: LlamaTokenizer |
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tokenizer_use_fast: true |
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tokenizer_legacy: true |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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push_dataset_to_hub: |
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hf_use_auth_token: true |
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datasets: |
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- path: datasets_cleansinng/datasets/helper_selector_1280_0305_v01.jsonl #Path to json dataset file in huggingface |
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#for type,conversation arguments read axolotl readme and pick what is suited for your project, I wanted a chatbot and put sharegpt and chatml |
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type: |
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system_prompt: "Instruction์ ๋ฐ๋ผ ์ ์ ํ๊ฒ Input ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ Output ๋ต๋ณ์ ํ์ธ์. ๋๋ ์ฌ์ฉ์ ์ง๋ฌธ(Instruction)์ ์ค์๊ฐ์ผ๋ก API ํธ์ถ์ ์ํ Json ํ์์ ๊ตฌ์กฐํ๋ ๊ฒฐ๊ณผ๋ฅผ ์์ฑํ๋ ์ธ๊ณต์ง๋ฅ์ด์ผ." |
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format: "[INST]### Instruction:\n{instruction}\n\n### Input:{input}\n\n[/INST]### Output: " |
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no_input_format: "[INST]### Instruction:\n{instruction}\n\n[/INST]### Output: " |
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field_instruction: Instruction |
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field_input: Input |
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field_output: Output |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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adapter: lora |
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lora_model_dir: |
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sequence_len: 4096 |
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sample_packing: |
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lora_r: 32 |
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lora_alpha: 32 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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- k_proj |
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- o_proj |
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- q_proj |
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- v_proj |
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lora_target_linear: |
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lora_fan_in_fan_out: |
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wandb_project: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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output_dir: ./output_solor/exp_16 |
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gradient_accumulation_steps: 8 |
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micro_batch_size: 8 |
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num_epochs: 5 |
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optimizer: adamw_torch |
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adam_beta2: 0.95 |
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adam_eps: 0.00001 |
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max_grad_norm: 1.0 |
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torchdistx_path: |
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lr_scheduler: cosine |
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lr_quadratic_warmup: true |
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learning_rate: 0.0005 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: false |
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fp16: false |
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float16: true |
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tf32: true |
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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: |
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sdp_attention: |
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flash_optimum: |
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warmup_steps: 100 |
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evals_per_epoch: 4 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: deepspeed_configs/zero1.json |
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weight_decay: 0.1 |
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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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# output_solor/exp_16 |
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This model is a fine-tuned version of [TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2015 |
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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: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.95) 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.3493 | 0.05 | 1 | 1.2795 | |
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| 1.2483 | 0.26 | 5 | 1.2769 | |
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| 1.2275 | 0.53 | 10 | 1.2099 | |
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| 1.0529 | 0.79 | 15 | 1.0724 | |
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| 0.8642 | 1.05 | 20 | 0.9709 | |
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| 0.8477 | 1.32 | 25 | 0.8245 | |
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| 0.7207 | 1.58 | 30 | 0.6994 | |
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| 0.4656 | 1.84 | 35 | 0.5878 | |
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| 0.4949 | 2.11 | 40 | 0.4970 | |
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| 0.3497 | 2.37 | 45 | 0.4221 | |
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| 0.3288 | 2.63 | 50 | 0.3672 | |
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| 0.3011 | 2.89 | 55 | 0.3250 | |
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| 0.2648 | 3.16 | 60 | 0.2900 | |
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| 0.3084 | 3.42 | 65 | 0.2591 | |
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| 0.2696 | 3.68 | 70 | 0.2459 | |
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| 0.2197 | 3.95 | 75 | 0.2286 | |
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| 0.1905 | 4.21 | 80 | 0.2111 | |
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| 0.1815 | 4.47 | 85 | 0.2084 | |
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| 0.2164 | 4.74 | 90 | 0.2128 | |
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| 0.1412 | 5.0 | 95 | 0.2015 | |
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### Framework versions |
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- PEFT 0.9.1.dev0 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.0 |
