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MetaMath Bagel DPO 34B by abacusai

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  Autotrain compatible   Conversational Dataset:abacusai/metamathfewsh...   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/abacusai/MetaMath-Bagel-DPO-34B 

MetaMath Bagel DPO 34B Benchmarks

ARC: 68.17 vs 96.7 (so35)-29.5%
HellaSwag: 84.23 vs 95.3 (gpt4)-11.6%
MMLU: 76.54 vs 88.3 (so35)-13.3%
TruthfulQA: 65.44 vs 59 (gpt4)10.9%
WinoGrande: 82.24 vs 87.5 (gpt4)-6%
GSM8K: 72.18 vs 96.4 (so35)-25.1%
LLME Score: 0.18077
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MetaMath Bagel DPO 34B (abacusai/MetaMath-Bagel-DPO-34B)

MetaMath Bagel DPO 34B Parameters and Internals

Model Type 
Fine-tuned Model
Training Details 
Data Sources:
Truthy DPO dataset
Methodology:
DPO Fine-tuning
LLM NameMetaMath Bagel DPO 34B
Repository 🤗https://huggingface.co/abacusai/MetaMath-Bagel-DPO-34B 
Model Size34b
Required VRAM69.2 GB
Updated2024-12-26
Maintainerabacusai
Model Typellama
Model Files  4.8 GB: 1-of-15   4.8 GB: 2-of-15   5.0 GB: 3-of-15   4.8 GB: 4-of-15   4.8 GB: 5-of-15   5.0 GB: 6-of-15   4.8 GB: 7-of-15   4.8 GB: 8-of-15   5.0 GB: 9-of-15   4.8 GB: 10-of-15   4.8 GB: 11-of-15   5.0 GB: 12-of-15   4.8 GB: 13-of-15   4.8 GB: 14-of-15   1.2 GB: 15-of-15
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length200000
Model Max Length200000
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typebfloat16

Quantized Models of the MetaMath Bagel DPO 34B

Model
Likes
Downloads
VRAM
MetaMath Bagel DPO 34B GGUF34512 GB

Best Alternatives to MetaMath Bagel DPO 34B

Best Alternatives
Context / RAM
Downloads
Likes
Casual Magnum 34B195K / 68.8 GB121
34B Beta195K / 69.2 GB334062
Yi 34B 200K195K / 68.9 GB4005317
Smaug 34B V0.1195K / 69.2 GB356360
Bagel Hermes 34B Slerp195K / 68.9 GB38041
Bagel 34B V0.2195K / 68.7 GB373939
Yi 34B 200K AEZAKMI V2195K / 69.2 GB110112
Smaug 34B V0.1 ExPO195K / 69.2 GB28320
Faro Yi 34B195K / 69.2 GB35496
Bagel DPO 34B V0.5195K / 68.7 GB283117
Note: green Score (e.g. "73.2") means that the model is better than abacusai/MetaMath-Bagel-DPO-34B.

Rank the MetaMath Bagel DPO 34B Capabilities

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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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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