94 lines
4.8 KiB
Markdown
94 lines
4.8 KiB
Markdown
---
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base_model: AIML-TUDA/QwenGuard-v1.2-7B
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datasets: AIML-TUDA/LlavaGuard
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extra_gated_fields:
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Affiliation: text
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Country: text
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Email: text
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? I have explicitly checked that downloading LlavaGuard is legal in my jurisdiction,
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in the country/region where I am located right now, and for the use case that
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I have described above, I have also read and accepted the relevant Terms of Use
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: checkbox
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Name: text
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extra_gated_prompt: By filling out the form below I understand that LlavaGuard is
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a derivative model based on webscraped images and the SMID dataset that use individual
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licenses and their respective terms and conditions apply. I understand that all
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content uses are subject to the terms of use. I understand that reusing the content
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in LlavaGuard might not be legal in all countries/regions and for all use cases.
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I understand that LlavaGuard is mainly targeted toward researchers and is meant
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to be used in research. LlavaGuard authors reserve the right to revoke my access
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to this data. They reserve the right to modify this data at any time in accordance
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with take-down requests.
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language:
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- en
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library_name: transformers
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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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- llama-factory
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- freeze
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- generated_from_trainer
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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/AIML-TUDA/QwenGuard-v1.2-7B
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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#QwenGuard-v1.2-7B-GGUF).***
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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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/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 1.0 | multi-modal supplement |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q2_K.gguf) | Q2_K | 3.1 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/QwenGuard-v1.2-7B-GGUF/resolve/main/QwenGuard-v1.2-7B.f16.gguf) | f16 | 15.3 | 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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