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Einstein V6 7B by Weyaxi

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  Autotrain compatible   Axolotl Base model:finetune:mistral-co... Base model:mistral-community/m...   Biology   Chatml   Chemistry   Conversational   Dataset:allenai/ai2 arc   Dataset:allenai/wildchat   Dataset:bigbio/med qa   Dataset:camel-ai/biology   Dataset:camel-ai/chemistry   Dataset:camel-ai/math   Dataset:camel-ai/physics Dataset:cot-alpaca-gpt4-from-o...   Dataset:derek-thomas/scienceqa Dataset:huggingfaceh4/no robot... Dataset:jondurbin/airoboros-3.... Dataset:knowrohit07/saraswati-...   Dataset:ldjnr/capybara   Dataset:lmsys/lmsys-chat-1m Dataset:m-a-p/codefeedback-fil...   Dataset:mandyyyyii/scibench Dataset:meta-math/metamathqa-4...   Dataset:metaeval/reclor Dataset:microsoft/orca-math-wo... Dataset:migtissera/synthia-v1....   Dataset:open-orca/slimorca Dataset:openassistant/oasst to...   Dataset:openbookqa Dataset:openchat/openchat shar...   Dataset:piqa   Dataset:sablo/oasst2 curated   Dataset:scibench   Dataset:sciq Dataset:stem-ai-mtl/electrical... Dataset:teknium/gpteacher-gene...   Dataset:tiger-lab/mathinstruct   Dataset:tiger-lab/scienceeval Dataset:totally-not-an-llm/eve... Dataset:wizardlm/wizardlm evol...   En   Endpoints compatible   Finetuned   Generated from trainer   Gpt4   Instruct   Math   Mistral   Model-index   Physics   Region:us   Safetensors   Science   Sharded   Synthetic data   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Weyaxi/Einstein-v6-7B 

Einstein V6 7B Benchmarks

ARC: 63.57 vs 96.7 (so35)-34.3%
HellaSwag: 82.76 vs 95.3 (gpt4)-13.2%
MMLU: 62.23 vs 88.3 (so35)-29.5%
TruthfulQA: 52.02 vs 59 (gpt4)-11.8%
WinoGrande: 78.61 vs 87.5 (gpt4)-10.2%
GSM8K: 63.53 vs 96.4 (so35)-34.1%
LLME Score: 0.1919
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Einstein V6 7B (Weyaxi/Einstein-v6-7B)

Einstein V6 7B Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model was trained using the Axolotl framework and the training was sponsored by sablo.ai.
Training Details 
Data Sources:
allenai/ai2_arc, camel-ai/physics, camel-ai/chemistry, camel-ai/biology, camel-ai/math, metaeval/reclor, openbookqa, mandyyyyii/scibench, derek-thomas/ScienceQA, TIGER-Lab/ScienceEval, jondurbin/airoboros-3.2, LDJnr/Capybara, Cot-Alpaca-GPT4-From-OpenHermes-2.5, STEM-AI-mtl/Electrical-engineering, knowrohit07/saraswati-stem, sablo/oasst2_curated, lmsys/lmsys-chat-1m, TIGER-Lab/MathInstruct, bigbio/med_qa, meta-math/MetaMathQA-40K, piqa, scibench, sciq, Open-Orca/SlimOrca, migtissera/Synthia-v1.3, allenai/WildChat, microsoft/orca-math-word-problems-200k, openchat/openchat_sharegpt4_dataset, teknium/GPTeacher-General-Instruct, m-a-p/CodeFeedback-Filtered-Instruction, totally-not-an-llm/EverythingLM-data-V3, HuggingFaceH4/no_robots, OpenAssistant/oasst_top1_2023-08-25, WizardLM/WizardLM_evol_instruct_70k
Context Length:
8192
Training Time:
2 epochs
Hardware Used:
8xRTX3090, 1xRTXA6000
Input Output 
Input Format:
ChatML
Accepted Modalities:
text
Output Format:
text
Release Notes 
Version:
v6
Notes:
Full fine-tuning of alpindale/Mistral-7B-v0.2-hf model using updated datasets and configurations as described. Sponsored by sablo.ai.
LLM NameEinstein V6 7B
Repository 🤗https://huggingface.co/Weyaxi/Einstein-v6-7B 
Base Model(s)  alpindale/Mistral-7B-v0.2-hf   alpindale/Mistral-7B-v0.2-hf
Model Size7b
Required VRAM14.4 GB
Updated2025-02-22
MaintainerWeyaxi
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3   0.0 GB
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32002
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Weyaxi/Einstein-v6-7B.

Rank the Einstein V6 7B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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