初始化项目，由ModelHub XC社区提供模型
Model: Weyaxi/Einstein-7B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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license: other
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tags:
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- axolotl
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- generated_from_trainer
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base_model: mistralai/Mistral-7B-v0.1
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datasets:
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- allenai/ai2_arc
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- camel-ai/physics
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- camel-ai/chemistry
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- camel-ai/biology
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- metaeval/reclor
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- openbookqa
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- mandyyyyii/scibench
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- derek-thomas/ScienceQA
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- wenhu/TheoremQA
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- TIGER-Lab/ScienceEval
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---
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# 🔬 Einstein-7B
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on datasets related to science.
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This model is fine-tuned using [QLoRa](https://arxiv.org/abs/2305.14314) and [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
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This model's training was sponsored by [sablo.ai](https://sablo.ai).
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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: mistralai/Mistral-7B-v0.1
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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: true
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strict: false
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datasets:
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- path: sci-datasets/arc_challange_train_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/camelai_biology_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/camelai_chemistry_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/camelai_physics_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/openbookqa_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/reclor_science_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/scibench_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/scienceqa_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/theoremqa_alpaca.json
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ds_type: json
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type: alpaca
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- path: sci-datasets/tiger_scienceeval_alpaca.json
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ds_type: json
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type: alpaca
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dataset_prepared_path: last_run_prepared
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val_set_size: 0
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output_dir: ./science-mistral
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adapter: qlora
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lora_model_dir:
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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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lora_r: 128
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lora_alpha: 64
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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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wandb_project: huggingface
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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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hub_model_id: Weyaxi/science-mistral
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# change #
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gradient_accumulation_steps: 12
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micro_batch_size: 6
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num_epochs: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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# change #
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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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saves_per_epoch: 3
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debug:
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deepspeed:
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weight_decay: 0.1
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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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# 📊 Datasets
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Following datasets were used in this model:
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- [ARC](https://huggingface.co/datasets/allenai/ai2_arc) (Note: Only **train** part)
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- [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)
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- [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)
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- [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)
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- [openbookqa](https://huggingface.co/datasets/openbookqa)
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- [reclor](https://huggingface.co/datasets/metaeval/reclor)
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- [scibench](https://github.com/mandyyyyii/scibench)
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- [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA)
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- [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA)
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- [ScienceEval](https://huggingface.co/datasets/TIGER-Lab/ScienceEval)
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# 💬 Prompt Template
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You can use this prompt template while using the model:
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### Alpaca
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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```
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# 🤝 Acknowledgments
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Thanks to Platypus for providing scripts to convert some of the datasets to Alpaca format: [Platypus/data_pipeline](https://github.com/arielnlee/Platypus/tree/main/data_pipeline)
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Thanks to all the dataset authors mentioned in the datasets section.
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Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.
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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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If you would like to support me:
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[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
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