167 lines
3.8 KiB
Markdown
167 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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tags:
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- generated_from_trainer
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base_model: leveldevai/TurdusBeagle-7B
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model-index:
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- name: Metabird-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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# Metabird-7B
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<details><summary>See axolotl config</summary>
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axolotl version: `0.3.0`
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```yaml
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base_model: leveldevai/TurdusBeagle-7B
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: 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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datasets:
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- path: shuyuej/metamath_gsm8k
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type:
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system_prompt: ""
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field_instruction: question
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field_output: answer
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format: "[INST] {instruction} [/INST]"
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no_input_format: "[INST] {instruction} [/INST]"
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./out
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sequence_len: 8192
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sample_packing: true
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pad_to_sequence_len: true
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eval_sample_packing: false
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wandb_project:
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wandb_entity:
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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: 4
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.000005
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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: 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: 1
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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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## Metabird
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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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This model is a fine-tuned version of [leveldevai/TurdusBeagle-7B](https://huggingface.co/leveldevai/TurdusBeagle-7B) on the shuyuej/metamath_gsm8k dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4017
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## Model description
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More information soon
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## Intended uses & limitations
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More information soon
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## Training and evaluation data
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More information soon
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## Training procedure
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More information soon
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-06
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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: 10
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.9074 | 0.05 | 1 | 0.9932 |
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| 0.5012 | 0.26 | 5 | 0.4849 |
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| 0.4204 | 0.53 | 10 | 0.4435 |
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| 0.3748 | 0.79 | 15 | 0.4017 |
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### Framework versions
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- Transformers 4.37.0.dev0
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- Pytorch 2.0.1+cu117
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Metabird-7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |71.03|
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|AI2 Reasoning Challenge (25-Shot)|69.54|
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|HellaSwag (10-Shot) |87.54|
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|MMLU (5-Shot) |65.27|
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|TruthfulQA (0-shot) |57.94|
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|Winogrande (5-shot) |83.03|
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|GSM8k (5-shot) |62.85|
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