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README.md
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---
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base_model: CausalLM/14B
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datasets:
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- JosephusCheung/GuanacoDataset
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- Open-Orca/OpenOrca
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- stingning/ultrachat
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- meta-math/MetaMathQA
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- liuhaotian/LLaVA-Instruct-150K
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- jondurbin/airoboros-3.1
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- WizardLM/WizardLM_evol_instruct_V2_196k
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- RyokoAI/ShareGPT52K
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- RyokoAI/Fandom23K
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- milashkaarshif/MoeGirlPedia_wikitext_raw_archive
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- wikipedia
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- wiki_lingua
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- fnlp/moss-003-sft-data
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- garage-bAInd/Open-Platypus
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- LDJnr/Puffin
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- openbmb/llava_zh
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- BAAI/COIG
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- TigerResearch/tigerbot-zhihu-zh-10k
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- liwu/MNBVC
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- teknium/openhermes
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inference: false
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language:
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- en
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- zh
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license: wtfpl
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model_creator: CausalLM
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model_name: CausalLM 14B
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model_type: llama
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pipeline_tag: text-generation
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prompt_template: '<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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'
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quantized_by: cgus
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tags:
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- llama
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- llama2
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---
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# CausalLM 14B - GPTQ
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- Model creator: [CausalLM](https://huggingface.co/CausalLM)
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- Original model: [CausalLM 14B](https://huggingface.co/CausalLM/14B)
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<!-- description start -->
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## Description
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Experimental exl2 quantization for CausalLM-14B for Exllamav2.
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I had some issues during quantization process, so I suspect it might have quality issues.
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3.5bpw version barely fits 12GB VRAM but has unusually high perplexity for wikitext dataset.
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I couldn't measure perplexity for 4bpw version and to compare it with TheBloke's GPTQ, so I have no idea if my quantization has issues or it supposed to be like this.
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You could try this exl2 version but I'd recommend to use [TheBloke's GPTQ](https://huggingface.co/TheBloke/CausalLM-14B-GPTQ) version instead.
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