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Bagel 34B V0.2 by jondurbin

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  Autotrain compatible   Conversational   Dataset:ai2 arc Dataset:allenai/ultrafeedback ...   Dataset:boolq   Dataset:cais/mmlu   Dataset:cakiki/rosetta-code   Dataset:codeparrot/apps   Dataset:datasets/winogrande   Dataset:drop   Dataset:facebook/belebele   Dataset:intel/orca dpo pairs Dataset:jondurbin/cinematika-v... Dataset:jondurbin/truthy-dpo-v...   Dataset:julielab/emobank   Dataset:kingbri/pippa-sharegpt   Dataset:ldjnr/capybara   Dataset:lmsys/lmsys-chat-1m Dataset:migtissera/synthia-v1.... Dataset:muennighoff/natural-in...   Dataset:nvidia/helpsteer   Dataset:open-orca/slimorca   Dataset:openbookqa   Dataset:piqa   Dataset:spider   Dataset:squad v2 Dataset:squish42/bluemoon-fand...   Dataset:tiger-lab/mathinstruct   Dataset:unalignment/spicy-3.1 Dataset:unalignment/toxic-dpo-... Dataset:vezora/tested-22k-pyth...   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/jondurbin/bagel-34b-v0.2 

Bagel 34B V0.2 Benchmarks

MMLU Pro: 39.33
GPQA: 8.95
MUSR: 11.96
BBH: 32.78
IFEval: 45.56 vs 88 (so35)-48.2%
ARC: 68.77 vs 96.7 (so35)-28.9%
HellaSwag: 83.72 vs 95.3 (gpt4)-12.2%
MMLU: 76.45 vs 88.3 (so35)-13.4%
TruthfulQA: 59.26 vs 59 (gpt4)0.4%
WinoGrande: 83.82 vs 87.5 (gpt4)-4.2%
GSM8K: 46.17 vs 96.4 (so35)-52.1%
MATH Lvl 5: 0.15
LLME Score: 0.23529
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bagel 34B V0.2 (jondurbin/bagel-34b-v0.2)

Bagel 34B V0.2 Parameters and Internals

Model Type 
Text Generation
Training Details 
Data Sources:
ai2_arc, unalignment/spicy-3.1, codeparrot/apps, facebook/belebele, boolq, jondurbin/cinematika-v0.1, drop, lmsys/lmsys-chat-1m, TIGER-Lab/MathInstruct, cais/mmlu, Muennighoff/natural-instructions, openbookqa, piqa, Vezora/Tested-22k-Python-Alpaca, cakiki/rosetta-code, Open-Orca/SlimOrca, spider, squad_v2, migtissera/Synthia-v1.3, datasets/winogrande, nvidia/HelpSteer, Intel/orca_dpo_pairs, unalignment/toxic-dpo-v0.1, jondurbin/truthy-dpo-v0.1, allenai/ultrafeedback_binarized_cleaned, Squish42/bluemoon-fandom-1-1-rp-cleaned, LDJnr/Capybara, JULIELab/EmoBank, kingbri/PIPPA-shareGPT
Methodology:
SFT phase using a variety of prompt formats: vicuna, llama-2, alpaca, and chat-ml (sorta).
Model Architecture:
unknown
Input Output 
Input Format:
vicuna, llama-2, alpaca, chat-ml
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Recommended to run for 1 epoch or 0.75 epoch, using a relatively low learning rate.
LLM NameBagel 34B V0.2
Repository 🤗https://huggingface.co/jondurbin/bagel-34b-v0.2 
Model Size34b
Required VRAM68.7 GB
Updated2025-04-28
Maintainerjondurbin
Model Typellama
Model Files  4.0 GB: 1-of-18   3.9 GB: 2-of-18   3.9 GB: 3-of-18   3.9 GB: 4-of-18   3.9 GB: 5-of-18   3.9 GB: 6-of-18   3.9 GB: 7-of-18   3.9 GB: 8-of-18   3.9 GB: 9-of-18   3.9 GB: 10-of-18   3.9 GB: 11-of-18   3.9 GB: 12-of-18   3.9 GB: 13-of-18   3.9 GB: 14-of-18   3.9 GB: 15-of-18   3.9 GB: 16-of-18   3.9 GB: 17-of-18   2.3 GB: 18-of-18
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 Bagel 34B V0.2

Model
Likes
Downloads
VRAM
Luminex 34B V0.1 EXL2 8.0bpw1434 GB
Bagel 34B V0.2 GGUF3126514 GB
Smaug 34B V0.1 3.0bpw H6 EXL221013 GB
Smaug 34B V0.1 8.0bpw H8 EXL211034 GB
Smaug 34B V0.1 4.0bpw H6 EXL23218 GB
Smaug 34B V0.1 2.65bpw H6 EXL21212 GB
Smaug 34B V0.1 2.4bpw H6 EXL21211 GB
Smaug 34B V0.1 AWQ2319 GB
Smaug 34B V0.1 GPTQ1221 GB
Smaug 34B V0.1 6.0bpw H6 EXL21226 GB

Best Alternatives to Bagel 34B V0.2

Best Alternatives
Context / RAM
Downloads
Likes
Yi 34B 200K195K / 68.9 GB40324317
Casual Magnum 34B195K / 68.8 GB21
Bagel Hermes 34B Slerp195K / 68.9 GB3501
34B Beta195K / 69.2 GB13763
Smaug 34B V0.1195K / 69.2 GB15060
Yi 34B 200K AEZAKMI V2195K / 69.2 GB18112
Mergekit Slerp Anaazls195K / 69.2 GB60
Yi 34B 200K HESOYAM 0905195K / 69.2 GB60
Merged RP Stew V2 34B195K / 68.9 GB18649
Faro Yi 34B195K / 69.2 GB1936
Note: green Score (e.g. "73.2") means that the model is better than jondurbin/bagel-34b-v0.2.

Rank the Bagel 34B V0.2 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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Release v20241227
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