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Bagel DPO 2.8B V0.2 by jondurbin

 »  All LLMs  »  jondurbin  »  Bagel DPO 2.8B V0.2   URL Share it on

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Base model:finetune:state-spac... Base model:state-spaces/mamba-...   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   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/jondurbin/bagel-dpo-2.8b-v0.2 

Bagel DPO 2.8B V0.2 Benchmarks

LLME Score: 0.15598
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bagel DPO 2.8B V0.2 (jondurbin/bagel-dpo-2.8b-v0.2)

Bagel DPO 2.8B V0.2 Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Areas:
Research, Commercial applications
Additional Notes 
The model supports several prompt formats and outputs by using specific tokenizer functions for template applications. The use of diversified prompt formats is designed to improve generalization in instruction handling.
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, gutenberg
Methodology:
Experimental fine-tune using a variety of datasets. Multiple epochs achieved by converting instructions into different prompt formats.
Model Architecture:
MambaLMHeadModel
Input Output 
Input Format:
Supports various prompt formats like vicuna, llama-2, alpaca, and chat-ml.
Output Format:
Generates text responses based on inputs following predefined prompts.
LLM NameBagel DPO 2.8B V0.2
Repository 🤗https://huggingface.co/jondurbin/bagel-dpo-2.8b-v0.2 
Base Model(s)  state-spaces/mamba-2.8b-slimpj   state-spaces/mamba-2.8b-slimpj
Model Size2.8b
Required VRAM11.1 GB
Updated2025-02-22
Maintainerjondurbin
Model Files  11.1 GB
Model ArchitectureAutoModel
Licenseapache-2.0
Tokenizer ClassGPTNeoXTokenizer
Padding Token</s>
Vocabulary Size50277

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Note: green Score (e.g. "73.2") means that the model is better than jondurbin/bagel-dpo-2.8b-v0.2.

Rank the Bagel DPO 2.8B V0.2 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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Release v20241227
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