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Model: cognitivecomputations/dolphin-2.9.3-llama-3-8b Source: Original Platform
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README.md
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README.md
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---
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base_model: meta-llama/Meta-Llama-3-8B
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tags:
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- generated_from_trainer
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model-index:
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- name: llama-3-8b-2.9.3
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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: meta-llama/Meta-Llama-3-8B
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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# load_in_4bit: true
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chat_template: chatml
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datasets:
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- path: /workspace/datasets/dolphin201-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/SystemChat_filtered_sharegpt.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/SystemChat_multilingual_sharegpt.jsonl
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type: sharegpt
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conversation: chatml
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# - path: /workspace/datasets/SystemChat-2.0-Arabic/SystemChatArabic_sharegpt.jsonl
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# type: sharegpt
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# conversation: chatml
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- path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/not_samantha_norefusals.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/Orca-Math-resort-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/agent_instruct_react_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/toolbench_instruct_j1s1_3k_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/toolbench_negative_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/toolbench_react_10p_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/toolbench_tflan_cot_30p_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/openhermes200k_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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output_dir: ./llama-3-8b-2.9.3
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sequence_len: 8192
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sample_packing: false
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pad_to_sequence_len: false
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# adapter: qlora
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# lora_r: 16
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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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# - q_proj
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# - k_proj
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# - v_proj
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# - o_proj
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# - gate_proj
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# - up_proj
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# - down_proj
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wandb_project: 2.9.3-llama-3-8b
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# wandb_entity: oaaic
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# wandb_watch:
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# wandb_name:
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# wandb_log_model:
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gradient_accumulation_steps: 8
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micro_batch_size: 2
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num_epochs: 3
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optimizer: adamw_8bit
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lr_scheduler: cosine
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learning_rate: 1e-5
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# max_grad_norm: 1.0
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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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tf32: false
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: true
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logging_steps: 1
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flash_attention: true
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deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
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warmup_steps: 10
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evals_per_epoch: 2
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saves_per_epoch: 2
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save_total_limit: 2
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weight_decay: 0.1
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special_tokens:
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eos_token: "<|im_end|>"
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pad_token: "<|end_of_text|>"
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tokens:
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- "<|im_start|>"
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- "<|im_end|>"
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```
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</details><br>
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/lucasatkins81/2.9.3-llama-3-8b/runs/tufa1uut)
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# llama-3-8b-2.9.3
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5771
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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: 1e-05
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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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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 16
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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: 10
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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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| 1.005 | 0.0001 | 1 | 0.9649 |
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| 0.6468 | 0.5000 | 5058 | 0.6022 |
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| 0.6648 | 1.0000 | 10116 | 0.5731 |
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| 0.4983 | 1.5000 | 15174 | 0.5668 |
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| 0.394 | 2.0000 | 20232 | 0.5478 |
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| 0.3182 | 2.4999 | 25290 | 0.5781 |
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| 0.2916 | 2.9999 | 30348 | 0.5771 |
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### Framework versions
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- Transformers 4.42.0.dev0
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- Pytorch 2.1.2+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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