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README.md
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---
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license:
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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: `0.4.0`
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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: 0.0005
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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: 0.3714
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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: 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 | 1.6236 |
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| 1.7038 | 0.5714 | 4 | 1.4355 |
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| 1.2833 | 0.8571 | 6 | 0.9288 |
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| 0.6389 | 1.1429 | 8 | 0.7050 |
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| 0.401 | 1.4286 | 10 | 0.5980 |
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| 0.2982 | 1.7143 | 12 | 0.5694 |
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| 0.3225 | 2.0 | 14 | 0.5651 |
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| 0.2214 | 2.2857 | 16 | 0.5221 |
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| 0.1375 | 2.5714 | 18 | 0.4537 |
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| 0.1058 | 2.8571 | 20 | 0.3971 |
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| 0.0945 | 3.1429 | 22 | 0.3698 |
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| 0.1352 | 3.4286 | 24 | 0.3518 |
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| 0.0688 | 3.7143 | 26 | 0.3420 |
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| 0.0677 | 4.0 | 28 | 0.3508 |
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| 0.0506 | 4.2857 | 30 | 0.3577 |
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| 0.1056 | 4.5714 | 32 | 0.3714 |
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| 0.0839 | 4.8571 | 34 | 0.3710 |
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| 0.0562 | 5.1429 | 36 | 0.3717 |
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| 0.0715 | 5.4286 | 38 | 0.3749 |
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| 0.0708 | 5.7143 | 40 | 0.3709 |
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| 0.07 | 6.0 | 42 | 0.3714 |
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.40.2
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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
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