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--- |
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base_model: Kortix/FastApply-1.5B-v1.0 |
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language: |
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- en |
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library_name: transformers |
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license: apache-2.0 |
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quantized_by: mradermacher |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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- sft |
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- fast-apply |
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- instant-apply |
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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/Kortix/FastApply-1.5B-v1.0 |
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<!-- provided-files --> |
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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/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q2_K.gguf) | Q2_K | 0.8 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q3_K_S.gguf) | Q3_K_S | 0.9 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q3_K_L.gguf) | Q3_K_L | 1.0 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.IQ4_XS.gguf) | IQ4_XS | 1.0 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q5_K_S.gguf) | Q5_K_S | 1.2 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q5_K_M.gguf) | Q5_K_M | 1.2 | | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q6_K.gguf) | Q6_K | 1.4 | very good quality | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality | |
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| [GGUF](https://huggingface.co/mradermacher/FastApply-1.5B-v1.0-GGUF/resolve/main/FastApply-1.5B-v1.0.f16.gguf) | f16 | 3.2 | 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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