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Smaug 34B V0.1 by abacusai

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Smaug 34B V0.1 is an open-source language model by abacusai. Features: 34b LLM, VRAM: 69.2GB, Context: 195K, License: apache-2.0, Merged, LLM Explorer Score: 0.22, Arc: 74.2, HellaSwag: 86.8, MMLU: 76.7, GSM8K: 72.2.

  Merged Model   Arxiv:2402.13228 Base model:finetune:jondurbin/... Base model:jondurbin/bagel-34b...   Conversational   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/abacusai/Smaug-34B-v0.1 

Smaug 34B V0.1 Benchmarks

ARC: 74.23 vs 96.7 (so35)-23.2%
HellaSwag: 86.76 vs 95.3 (gpt4)-9%
MMLU: 76.66 vs 88.3 (so35)-13.2%
GSM8K: 72.18 vs 96.4 (so35)-25.1%
LLME Score: 0.22183
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Smaug 34B V0.1 (abacusai/Smaug-34B-v0.1)
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Smaug 34B V0.1 Parameters and Internals

Model Type 
finetune
Additional Notes 
Smaug-34B-v0.1 leverages a new fine-tuning technique, DPO-Positive (DPOP), which is superior in reducing the likelihood of the preferred examples being minimized while enhancing probability between preferred and dispreferred classes.
Training Details 
Data Sources:
ARC, HellaSwag, MetaMath
Methodology:
Fine-tuning using DPO-Positive (DPOP) and pairwise preference versions
LLM NameSmaug 34B V0.1
Repository 🤗https://huggingface.co/abacusai/Smaug-34B-v0.1 
Base Model(s)  jondurbin/bagel-34b-v0.2   jondurbin/bagel-34b-v0.2
Merged ModelYes
Model Size34b
Required VRAM69.2 GB
Updated2026-05-28
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

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Note: green Score (e.g. "73.2") means that the model is better than abacusai/Smaug-34B-v0.1.

Rank the Smaug 34B V0.1 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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