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README.md
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- Not presented here, but this model outperforms the base llama-2-13b on MMLU-fs with a score of 58.3. If this score ends up being be replicated on the HF LLM leaderboard, **this would place this model at 2nd or 3rd overall for MMLU among 13b models (and the #1 for extended context)**
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- Feedback regarding real-world performance is appreciated. Llama2-13b is known to have repetition problems. Does the extensive training on top of the base model help ameliorate this tendency? Perplexity and MMLU are great, but the don't tell the whole story.
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## Prompting:
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This model was trained with airoboros-like prompting in the 2nd phase. See the following from one of Jon Durbin's airoboros model cards:
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- Not presented here, but this model outperforms the base llama-2-13b on MMLU-fs with a score of 58.3. If this score ends up being be replicated on the HF LLM leaderboard, **this would place this model at 2nd or 3rd overall for MMLU among 13b models (and the #1 for extended context)**
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- Feedback regarding real-world performance is appreciated. Llama2-13b is known to have repetition problems. Does the extensive training on top of the base model help ameliorate this tendency? Perplexity and MMLU are great, but the don't tell the whole story.
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## Quantization:
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The merged model was quantized with AutoGPTQ (bits = 4, group_size = 32, desc_act = True).
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## Prompting:
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This model was trained with airoboros-like prompting in the 2nd phase. See the following from one of Jon Durbin's airoboros model cards:
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