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Bagel 7B V0.5 by jondurbin

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  Autotrain compatible Base model:finetune:mistral-co... Base model:mistral-community/m...   Conversational   Dataset:ai2 arc Dataset:allenai/ultrafeedback ... Dataset:argilla/distilabel-int... Dataset:b-mc2/sql-create-conte... Dataset:bluemoon-fandom-1-1-rp...   Dataset:boolq   Dataset:cakiki/rosetta-code   Dataset:camel-ai/biology   Dataset:camel-ai/chemistry   Dataset:camel-ai/math   Dataset:camel-ai/physics   Dataset:codeparrot/apps   Dataset:facebook/belebele Dataset:glaiveai/glaive-functi... Dataset:grimulkan/limarp-augme... Dataset:jondurbin/airoboros-3.... Dataset:jondurbin/cinematika-v... Dataset:jondurbin/contextual-d... Dataset:jondurbin/gutenberg-dp...   Dataset:jondurbin/py-dpo-v0.1 Dataset:jondurbin/truthy-dpo-v...   Dataset:kingbri/pippa-sharegpt   Dataset:ldjnr/capybara   Dataset:lmsys/lmsys-chat-1m Dataset:mattpscott/airoboros-s... Dataset:migtissera/synthia-v1.... Dataset:muennighoff/natural-in...   Dataset:open-orca/slimorca   Dataset:openbookqa Dataset:parisneo/lollms aware ...   Dataset:piqa   Dataset:ropes   Dataset:squad v2   Dataset:tiger-lab/mathinstruct Dataset:unalignment/toxic-dpo-... Dataset:vezora/tested-22k-pyth... Dataset:whiterabbitneo/wrn-cha... Dataset:whiterabbitneo/wrn-cha...   Dataset:winogrande Dataset:wizardlm/wizardlm evol...   Endpoints compatible   Instruct   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/jondurbin/bagel-7b-v0.5 

Bagel 7B V0.5 Benchmarks

ARC: 63.65 vs 96.7 (so35)-34.2%
HellaSwag: 83.62 vs 95.3 (gpt4)-12.3%
MMLU: 64.93 vs 88.3 (so35)-26.5%
TruthfulQA: 55.76 vs 59 (gpt4)-5.5%
WinoGrande: 80.98 vs 87.5 (gpt4)-7.5%
GSM8K: 50.34 vs 96.4 (so35)-47.8%
LLME Score: 0.19352
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bagel 7B V0.5 (jondurbin/bagel-7b-v0.5)

Bagel 7B V0.5 Parameters and Internals

Model Type 
text generation, fine-tune
Additional Notes 
The model supports a wide range of task-specific prompts, including context obedience question answering, summarization, function calling, and more.
Training Details 
Data Sources:
ai2_arc, allenai/ultrafeedback_binarized_cleaned, argilla/distilabel-intel-orca-dpo-pairs, jondurbin/airoboros-3.2, codeparrot/apps, facebook/belebele, bluemoon-fandom-1-1-rp-cleaned, boolq, camel-ai/biology, camel-ai/chemistry, camel-ai/math, camel-ai/physics, jondurbin/contextual-dpo-v0.1, jondurbin/gutenberg-dpo-v0.1, jondurbin/py-dpo-v0.1, jondurbin/truthy-dpo-v0.1, LDJnr/Capybara, jondurbin/cinematika-v0.1, WizardLM/WizardLM_evol_instruct_70k, glaiveai/glaive-function-calling-v2, grimulkan/LimaRP-augmented, lmsys/lmsys-chat-1m, ParisNeo/lollms_aware_dataset, TIGER-Lab/MathInstruct, Muennighoff/natural-instructions, openbookqa, kingbri/PIPPA-shareGPT, piqa, Vezora/Tested-22k-Python-Alpaca, ropes, cakiki/rosetta-code, Open-Orca/SlimOrca, b-mc2/sql-create-context, squad_v2, mattpscott/airoboros-summarization, migtissera/Synthia-v1.3, unalignment/toxic-dpo-v0.2, WhiteRabbitNeo/WRN-Chapter-1, WhiteRabbitNeo/WRN-Chapter-2, winogrande
Methodology:
Fine-tuned using SFT data sources with decontamination by cosine similarity against common benchmarks. Multiple prompt formats were used in training: vicuna, llama-2, alpaca, and a modified chat-ml.
Input Output 
Input Format:
Multiple prompt formats used: Llama-2, Alpaca, Vicuna, ChatML.
Accepted Modalities:
text
Output Format:
Text outputs, JSON for function calls and VAD scores.
LLM NameBagel 7B V0.5
Repository 🤗https://huggingface.co/jondurbin/bagel-7b-v0.5 
Base Model(s)  alpindale/Mistral-7B-v0.2-hf   alpindale/Mistral-7B-v0.2-hf
Model Size7b
Required VRAM14.4 GB
Updated2025-02-22
Maintainerjondurbin
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than jondurbin/bagel-7b-v0.5.

Rank the Bagel 7B V0.5 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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