NLPinas/yi-bagel-2x34b
NLPinas/yi-bagel-2x34b is a 34 billion parameter merged language model created by NLPinas, combining jondurbin/bagel-dpo-34b-v0.2 and jondurbin/nontoxic-bagel-34b-v0.2 using a linear merge method. This model debuted at rank #4 on the Open LLM Leaderboard (January 11, 2024), demonstrating strong performance across various reasoning and commonsense benchmarks. It is designed to assess the impact of DPO on censoring and offers a balanced approach to general language understanding and generation.
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Model Overview
NLPinas/yi-bagel-2x34b is a 34 billion parameter language model developed by NLPinas, created through an experimental linear merge of two distinct models: jondurbin/bagel-dpo-34b-v0.2 and jondurbin/nontoxic-bagel-34b-v0.2. This merge aims to evaluate the influence of DPO (Direct Preference Optimization) on model behavior, particularly concerning censoring.
Key Capabilities & Performance
This model achieved a notable rank #4 on the Hugging Face Open LLM Leaderboard as of January 11, 2024, showcasing robust performance across a suite of benchmarks:
- MMLU (5-shot): 76.60
- ARC (25-shot): 72.70
- HellaSwag (10-shot): 85.44
- TruthfulQA (0-shot): 71.42
- Winogrande (5-shot): 82.72
- GSM8K (5-shot): 60.73
These metrics indicate strong abilities in multitask accuracy, scientific reasoning, commonsense inference, truthfulness, and mathematical problem-solving.
Unique Characteristics
- Merged Architecture: Utilizes a linear merge of two specialized models, offering a unique blend of their respective strengths.
- DPO Impact Assessment: Specifically designed to explore the effects of DPO on model outputs, particularly regarding content moderation and bias.
Use Cases
Given its balanced performance and experimental nature, yi-bagel-2x34b is suitable for:
- General-purpose language generation and understanding tasks.
- Research into model merging techniques and the impact of DPO.
- Applications requiring a model with strong reasoning and commonsense capabilities.