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Model: MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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tags:
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- generated_from_trainer
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- fine-tuned
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- wikihow
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- cosmopedia
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- qwen
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- moe
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base_model: Qwen/Qwen1.5-MoE-A2.7B
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datasets:
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- HuggingFaceTB/cosmopedia
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pipeline_tag: text-generation
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model-index:
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- name: models/Qwen1.5-MoE-A2.7B-Wikihow
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results: []
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---
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# models/Qwen1.5-MoE-A2.7B-Wikihow
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This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) dataset.
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## How to use it
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```python
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# Use a pipeline as a high-level helper
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from transformers import pipeline
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pipe = pipeline("text-generation", model="MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
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```
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```python
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
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```
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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: 0.0002
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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: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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- total_eval_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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<!-- 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: Qwen/Qwen1.5-MoE-A2.7B
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trust_remote_code: true
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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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# hub_model_id: MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow
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# hf_use_auth_token: true
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chat_template: chatml
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datasets:
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- path: HuggingFaceTB/cosmopedia
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name: wikihow
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type:
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system_prompt: ""
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field_instruction: prompt
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field_output: text
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format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
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no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
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dataset_prepared_path:
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val_set_size: 0.0
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output_dir: ./models/Qwen1.5-MoE-A2.7B-Wikihow
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len: false
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adapter: lora
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lora_model_dir:
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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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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: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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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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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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```
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</details><br>
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.40.0.dev0
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- Pytorch 2.2.0+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Qwen1.5-MoE-A2.7B-Wikihow)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |11.43|
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|IFEval (0-Shot) |29.54|
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|BBH (3-Shot) |15.47|
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|MATH Lvl 5 (4-Shot)| 2.87|
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|GPQA (0-shot) | 3.36|
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|MuSR (0-shot) | 2.01|
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|MMLU-PRO (5-shot) |15.34|
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