100 lines
4.3 KiB
Markdown
100 lines
4.3 KiB
Markdown
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---
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base_model: huihui-ai/c4ai-command-r7b-12-2024-abliterated
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extra_gated_fields:
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Affiliation: text
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Country: country
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I agree to use this model for non-commercial use ONLY: checkbox
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Name: text
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extra_gated_prompt: By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and
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acknowledge that the information you provide will be collected, used, and shared
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in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll
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receive email updates about C4AI and Cohere research, events, products and services.
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You can unsubscribe at any time.
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- ja
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- ko
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- zh
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- ar
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- el
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- fa
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- pl
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- id
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- cs
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- he
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- hi
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- nl
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- ro
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- ru
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- tr
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- uk
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- vi
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library_name: transformers
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license: cc-by-nc-4.0
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quantized_by: mradermacher
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tags:
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- abliterated
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- uncensored
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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/huihui-ai/c4ai-command-r7b-12-2024-abliterated
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-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/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q2_K.gguf) | Q2_K | 3.5 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q3_K_M.gguf) | Q3_K_M | 4.3 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q3_K_L.gguf) | Q3_K_L | 4.6 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q4_K_M.gguf) | Q4_K_M | 5.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q5_K_M.gguf) | Q5_K_M | 5.9 | |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/c4ai-command-r7b-12-2024-abliterated-GGUF/resolve/main/c4ai-command-r7b-12-2024-abliterated.f16.gguf) | f16 | 16.2 | 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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