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--- |
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license: other |
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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: deepseek-ai/deepseek-coder-1.3b-instruct |
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model-index: |
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- name: deepseek-code-1.3b-inst-NLQ2Cypher |
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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: `0.4.1` |
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```yaml |
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base_model: deepseek-ai/deepseek-coder-1.3b-instruct |
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# base_model: Qwen/CodeQwen1.5-7B-Chat |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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is_mistral_derived_model: 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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lora_fan_in_fan_out: false |
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data_seed: 49 |
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seed: 49 |
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datasets: |
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- path: sample_data/alpaca_synth_cypher.jsonl |
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type: sharegpt |
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conversation: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.1 |
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output_dir: ./qlora-alpaca-deepseek-1.3b-inst |
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# output_dir: ./qlora-alpaca-out |
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# hub_model_id: jermyn/CodeQwen1.5-7B-Chat-NLQ2Cypher |
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hub_model_id: jermyn/deepseek-code-1.3b-inst-NLQ2Cypher |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 896 |
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sample_packing: false |
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pad_to_sequence_len: 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_linear: true |
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lora_fan_in_fan_out: |
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lora_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens. |
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# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models. |
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# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities. |
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# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994 |
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# lora_modules_to_save: |
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# - embed_tokens |
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# - lm_head |
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wandb_project: fine-tune-axolotl |
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wandb_entity: jermyn |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 16 |
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eval_batch_size: 16 |
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num_epochs: 6 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.001 |
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max_grad_norm: 1.0 |
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adam_beta2: 0.95 |
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adam_epsilon: 0.00001 |
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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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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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# saves_per_epoch: 6 |
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save_steps: 10 |
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save_total_limit: 3 |
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debug: |
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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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save_safetensors: true |
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``` |
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</details><br> |
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# deepseek-code-1.3b-inst-NLQ2Cypher |
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This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3816 |
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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: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 49 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.8723 | 0.1429 | 1 | 1.6354 | |
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| 1.9222 | 0.2857 | 2 | 1.5979 | |
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| 1.5222 | 0.5714 | 4 | 1.0739 | |
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| 0.7251 | 0.8571 | 6 | 0.7059 | |
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| 0.3581 | 1.1429 | 8 | 0.6420 | |
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| 0.2804 | 1.4286 | 10 | 0.6576 | |
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| 0.2337 | 1.7143 | 12 | 0.4628 | |
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| 0.1988 | 2.0 | 14 | 0.3674 | |
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| 0.1218 | 2.2857 | 16 | 0.3528 | |
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| 0.1087 | 2.5714 | 18 | 0.3584 | |
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| 0.0842 | 2.8571 | 20 | 0.3569 | |
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| 0.0733 | 3.1429 | 22 | 0.3610 | |
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| 0.0943 | 3.4286 | 24 | 0.3477 | |
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| 0.058 | 3.7143 | 26 | 0.3575 | |
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| 0.0753 | 4.0 | 28 | 0.3797 | |
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| 0.0435 | 4.2857 | 30 | 0.3915 | |
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| 0.0758 | 4.5714 | 32 | 0.3902 | |
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| 0.0608 | 4.8571 | 34 | 0.3875 | |
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| 0.0368 | 5.1429 | 36 | 0.3853 | |
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| 0.0444 | 5.4286 | 38 | 0.3818 | |
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| 0.055 | 5.7143 | 40 | 0.3820 | |
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| 0.0532 | 6.0 | 42 | 0.3816 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
