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Model: mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF Source: Original Platform
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---
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base_model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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library_name: transformers
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license: apache-2.0
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- chat
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: -->
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static quants of https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF).***
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-i1-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q3_K_S.gguf) | Q3_K_S | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q2_K.gguf) | Q2_K | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q5_K_S.gguf) | Q5_K_S | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q5_K_M.gguf) | Q5_K_M | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q6_K.gguf) | Q6_K | 0.8 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.Q8_0.gguf) | Q8_0 | 0.8 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1.f16.gguf) | f16 | 1.4 | 16 bpw, overkill |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time.
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<!-- end -->
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