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README.md
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tags:
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- axolotl
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---
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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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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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##
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##
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- learning_rate: 1e-05
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- train_batch_size: 6
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- eval_batch_size: 6
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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: 4
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- total_train_batch_size: 192
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- total_eval_batch_size: 48
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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: 2
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- Pytorch 2.4.0.dev20240508+rocm6.1
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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---
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language:
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- en
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license: other
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tags:
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- axolotl
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- instruct
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- finetune
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- chatml
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- gpt4
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- synthetic data
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- science
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- physics
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- chemistry
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- biology
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- math
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- qwen
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- qwen2
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base_model: Qwen/Qwen2-7B
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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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- camel-ai/math
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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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- TIGER-Lab/ScienceEval
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- jondurbin/airoboros-3.2
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- LDJnr/Capybara
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- Cot-Alpaca-GPT4-From-OpenHermes-2.5
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- STEM-AI-mtl/Electrical-engineering
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- knowrohit07/saraswati-stem
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- sablo/oasst2_curated
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- lmsys/lmsys-chat-1m
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- TIGER-Lab/MathInstruct
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- bigbio/med_qa
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- meta-math/MetaMathQA-40K
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- openbookqa
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- piqa
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- metaeval/reclor
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- derek-thomas/ScienceQA
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- scibench
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- sciq
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- Open-Orca/SlimOrca
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- migtissera/Synthia-v1.3
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- TIGER-Lab/ScienceEval
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- allenai/WildChat
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- microsoft/orca-math-word-problems-200k
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- openchat/openchat_sharegpt4_dataset
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- teknium/GPTeacher-General-Instruct
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- m-a-p/CodeFeedback-Filtered-Instruction
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- totally-not-an-llm/EverythingLM-data-V3
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- HuggingFaceH4/no_robots
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- OpenAssistant/oasst_top1_2023-08-25
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- WizardLM/WizardLM_evol_instruct_70k
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- abacusai/SystemChat-1.1
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- H-D-T/Buzz-V1.2
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---
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# 🔬 Einstein-v7-Qwen2-7B
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This model is a full fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on diverse datasets.
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This model is finetuned using `8xMI300X` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
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This model's training was sponsored by [sponsor](https://sponsor).
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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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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# 💬 Prompt Template
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You can use ChatML prompt template while using the model:
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### ChatML
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```
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<|im_start|>system
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{system}<|im_end|>
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<|im_start|>user
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{user}<|im_end|>
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<|im_start|>assistant
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{asistant}<|im_end|>
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```
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This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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`tokenizer.apply_chat_template()` method:
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```python
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messages = [
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{"role": "system", "content": "You are helpful AI asistant."},
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{"role": "user", "content": "Hello!"}
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]
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gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
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model.generate(**gen_input)
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```
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# 📊 Datasets used in this model
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The datasets used to train this model are listed in the metadata section of the model card.
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Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.
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The results of this filtering process and its outcomes are in the data folder of this repository:
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[Weyaxi/Einstein-v7-Qwen2-7B/data](https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B/tree/main/data)
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# 🔄 Quantizationed versions
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## GGUF [@bartowski](https://huggingface.co/bartowski)
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- https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF
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## ExLlamaV2 [@bartowski](https://huggingface.co/bartowski)
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- https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-exl2
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# 🎯 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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# 🤖 Additional information about training
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This model is full fine-tuned for 2 epoch.
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Total number of steps was 500.
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<details><summary>Loss graph</summary>
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</details><br>
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# 🤝 Acknowledgments
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Thanks to [sponsor](https://sponsor) for sponsoring this model.
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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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Thanks to all open source AI community.
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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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