MM OV Bagel DPO 34B C1000 250 by abacusai

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  Autotrain compatible   Conversational Dataset:abacusai/metamathfewsh...   Endpoints compatible   License:apache-2.0   Llama   Region:us   Safetensors   Sharded   Tensorflow

MM OV Bagel DPO 34B C1000 250 Benchmarks

MM OV Bagel DPO 34B C1000 250 Parameters and Internals

LLM NameMM OV Bagel DPO 34B C1000 250
RepositoryOpen on ๐Ÿค— 
Model Size34b
Required VRAM69.2 GB
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
Context Length200000
Model Max Length200000
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typefloat16
MM OV Bagel DPO 34B C1000 250 (abacusai/MM-OV-bagel-DPO-34b-c1000-250)

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HF Rank
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Smaug 34B V0.10.3195K / 69.2 GB331858
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Faro Yi 34B0.3195K / 69.2 GB27176
Bagel DPO 34B V0.50.3195K / 68.7 GB161417
Yi 34B 200K HESOYAM 09050.3195K / 69.2 GB3660
Nous Capybara 34B0.3195K / 68.9 GB7313240
Bagel DPO 34B V0.20.3195K / 69.2 GB282297
Bagel 34B V0.20.3195K / 68.7 GB552837
Deepmoney 34B 200K Base0.3195K / 68.9 GB110552
Note: green Score (e.g. "73.2") means that the model is better than abacusai/MM-OV-bagel-DPO-34b-c1000-250.

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Data Analysis and Insight Generation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024071601