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README.md
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---
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tags:
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- axolotl
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- generated_from_trainer
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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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base_model: alpindale/Mistral-7B-v0.2-hf
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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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language:
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- en
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/CxDk4KKhQqL-Pg0AMn1gb.png)
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<center><h1>📝 Note 📝</h1></center>
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📢 This model is currently in 1.5 epoch and this is a pre release. Main release will be available in 1 days.
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-------------
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# 🔬 Einstein-v6-7B
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This model
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This model is finetuned using `8xRTX3090` + `1xRTXA6000` using [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: `0.4.0`
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unk_token: "<unk>"
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tokens:
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- "<|im_start|>"
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```
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</details><br>
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# 💬 Prompt Template
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You can use this 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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# 🔄 Quantizationed versions
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- https://huggingface.co/bartowski/Einstein-v6-7B-GGUF
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## ExLlamaV2 [@bartowski](https://huggingface.co/bartowski)
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- https://huggingface.co/bartowski/Einstein-v6-7B-exl2
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#
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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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---
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base_model: alpindale/Mistral-7B-v0.2-hf
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: Einstein-v6-7B
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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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[<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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unk_token: "<unk>"
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tokens:
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- "<|im_start|>"
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```
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</details><br>
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# Einstein-v6-7B
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This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the None dataset.
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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: 5e-06
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 9
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 36
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- total_eval_batch_size: 9
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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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### Training results
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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