macadeliccc/WestLake-7B-v2-laser-truthy-dpo
macadeliccc/WestLake-7B-v2-laser-truthy-dpo is a 7 billion parameter language model fine-tuned from cognitivecomputations/WestLake-7B-v2-laser using the jondurbin/truthy-dpo-v0.1 dataset. This model is optimized for truthfulness and helpfulness, achieving an average score of 75.37 on the Open LLM Leaderboard. It is suitable for general conversational AI applications requiring accurate and safe responses, with a context length of 8192 tokens.
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Model Overview
macadeliccc/WestLake-7B-v2-laser-truthy-dpo is a 7 billion parameter language model built upon the cognitivecomputations/WestLake-7B-v2-laser base model. It underwent a fine-tuning process using the jondurbin/truthy-dpo-v0.1 dataset over two epochs with a learning rate of 2e-5, aiming to enhance its truthfulness and alignment.
Key Capabilities & Performance
This model demonstrates strong performance across various benchmarks, as evaluated on the Open LLM Leaderboard:
- Average Score: 75.37
- AI2 Reasoning Challenge (25-Shot): 73.89
- HellaSwag (10-Shot): 88.85
- MMLU (5-Shot): 64.84
- TruthfulQA (0-shot): 69.81
- Winogrande (5-shot): 86.66
- GSM8k (5-shot): 68.16
An evaluation of its GGUF version using EQ-Bench reported a score of 75.15. The model supports a context length of 8192 tokens.
Usage and Integrations
The model is available in various quantized formats, including GGUF and ExLlamav2 (3.5 through 8 bpw) provided by bartowski. It is designed to work with a ChatML-aligned prompt template, although the README notes that ChatML templating may not function as expected directly within the transformers library, providing a specific code example for proper multi-turn conversation handling.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.