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Bagel 8x7b V0.2 GGUF by TheBloke

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Base model:jondurbin/bagel-8x7... Base model:quantized:jondurbin...   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...   Gguf   Mixtral   Moe   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/bagel-8x7b-v0.2-GGUF 

Bagel 8x7b V0.2 GGUF Benchmarks

LLME Score: 0.16032
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bagel 8x7b V0.2 GGUF (TheBloke/bagel-8x7b-v0.2-GGUF)

Bagel 8x7b V0.2 GGUF Parameters and Internals

Model Type 
mixtral
Additional Notes 
The model includes examples of function/args generation and execution planning output format is JSON or YAML.
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-tune using bagel. After SFT phase, before DPO has been applied.
Context Length:
32768
Training Time:
4 days, 15 hours, 6 minutes, and 42 seconds
Hardware Used:
8x A6000 GPUs
Model Architecture:
Mixtral-8x7b
Input Output 
Input Format:
Prompt format varies. Includes Alpaca, Vicuna, chat-ML, and Llama-2 formats.
Accepted Modalities:
text
Output Format:
As per prompt instructions for various formats.
Performance Tips:
Use a very low temperature for specific tasks (e.g., RA(G)/contextual question answering).
Release Notes 
Version:
0.2
Notes:
Experimental fine-tune using bagel. Supports multiple prompt formats.
LLM NameBagel 8x7b V0.2 GGUF
Repository 🤗https://huggingface.co/TheBloke/bagel-8x7b-v0.2-GGUF 
Model NameBagel 8X7B v0.2
Model CreatorJon Durbin
Base Model(s)  jondurbin/bagel-8x7b-v0.2   jondurbin/bagel-8x7b-v0.2
Model Size1m
Required VRAM15.6 GB
Updated2025-02-05
MaintainerTheBloke
Model Typemixtral
Model Files  15.6 GB   20.4 GB   26.4 GB   26.4 GB   32.2 GB   32.2 GB   38.4 GB   49.6 GB
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licenseapache-2.0

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/bagel-8x7b-v0.2-GGUF.

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