aashish1904
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README.md
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---
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base_model:
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- Qwen/Qwen2.5-14B-Instruct
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- Lambent/qwen2.5-lumen-rebased-14B
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
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# QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF
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This is quantized version of [Lambent/qwen2.5-reinstruct-alternate-lumen-14B](https://huggingface.co/Lambent/qwen2.5-reinstruct-alternate-lumen-14B) created using llama.cpp
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# Original Model Card
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# qwenreinstruct
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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Extracted an approximate LoRA of v000000/Qwen2.5-Lumen-14B, rank 128 difference between that and Instruct,
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and first applied this to Lambent/qwen2.5-14B-alternate-instruct-slerp which had no issues with EQ-Bench.
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Then, here, re-applied a density and weight of original Instruct which in previous merges gave me no issues with EQ-Bench.
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This one has EQ-Bench of 77.6713 and no "emotions don't match reference error" (if possibly still one not parsed).
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This is similar to Lumen and original Instruct and slightly exceeds both (within margin of error).
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My hope is that it has healed Instruct somewhat and regained its intelligence.
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### Merge Method
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This model was merged using the della merge method using [Lambent/qwen2.5-lumen-rebased-14B](https://huggingface.co/Lambent/qwen2.5-lumen-rebased-14B) as a base.
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### Models Merged
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The following models were included in the merge:
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* [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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models:
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- model: Qwen/Qwen2.5-14B-Instruct
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parameters:
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weight: 0.3
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density: 0.4
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merge_method: della
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base_model: Lambent/qwen2.5-lumen-rebased-14B
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parameters:
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epsilon: 0.05
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lambda: 1
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dtype: bfloat16
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tokenizer_source: base
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```
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