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Bagel DPO 8x7b V0.2 6.0bpw H6 EXL2 by LoneStriker

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Bagel DPO 8x7b V0.2 6.0bpw H6 EXL2 is an open-source language model by LoneStriker. Features: 1m LLM, VRAM: 35.3GB, Context: 32K, License: apache-2.0, MoE, Quantized, LLM Explorer Score: 0.11.

  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/airoboros-3.... 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/toxic-dpo-... Dataset:vezora/tested-22k-pyth...   Endpoints compatible   Exl2   Mixtral   Moe   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/LoneStriker/bagel-dpo-8x7b-v0.2-6.0bpw-h6-exl2 

Bagel DPO 8x7b V0.2 6.0bpw H6 EXL2 Benchmarks

LLME Score: 0.11392
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bagel DPO 8x7b V0.2 6.0bpw H6 EXL2 (LoneStriker/bagel-dpo-8x7b-v0.2-6.0bpw-h6-exl2)
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Bagel DPO 8x7b V0.2 6.0bpw H6 EXL2 Parameters and Internals

Model Type 
text generation, instruction following
Additional Notes 
Model utilizes a novel prompt mixing strategy for more robust generation.
Training Details 
Data Sources:
ai2_arc, jondurbin/airoboros-3.2, 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:
Experimental fine-tuning using SFT and DPO methods.
Context Length:
2048
Hardware Used:
Massed Compute 4xA6000
Input Output 
Input Format:
Multiple formats: Alpaca, Vicuna, ChatML, Llama-2 Chat
Accepted Modalities:
text
Output Format:
Textual responses
LLM NameBagel DPO 8x7b V0.2 6.0bpw H6 EXL2
Repository 🤗https://huggingface.co/LoneStriker/bagel-dpo-8x7b-v0.2-6.0bpw-h6-exl2 
Model Size1m
Required VRAM35.3 GB
Updated2025-09-23
MaintainerLoneStriker
Model Typemixtral
Model Files  8.6 GB: 1-of-5   8.6 GB: 2-of-5   8.6 GB: 3-of-5   8.6 GB: 4-of-5   0.9 GB: 5-of-5
Quantization Typeexl2
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/bagel-dpo-8x7b-v0.2-6.0bpw-h6-exl2.

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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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