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/dev/null +++ b/README.md @@ -0,0 +1,82 @@ +--- +base_model: lingshu-medical-mllm/Lingshu-7B +language: +- en +library_name: transformers +license: mit +mradermacher: + readme_rev: 1 +quantized_by: mradermacher +tags: +- medical +- multimodal +- report generation +- radiology +- clinical-reasoning +- MRI +- CT +- Histopathology +- X-ray +- Fundus +--- +## About + + + + + + +static quants of https://huggingface.co/lingshu-medical-mllm/Lingshu-7B + + + +***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Lingshu-7B-GGUF).*** + +weighted/imatrix quants are available at https://huggingface.co/mradermacher/Lingshu-7B-i1-GGUF +## Usage + +If you are unsure how to use GGUF files, refer to one of [TheBloke's +READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for +more details, including on how to concatenate multi-part files. + +## Provided Quants + +(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) + +| Link | Type | Size/GB | Notes | +|:-----|:-----|--------:|:------| +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 1.0 | multi-modal supplement | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q2_K.gguf) | Q2_K | 3.1 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | +| [GGUF](https://huggingface.co/mradermacher/Lingshu-7B-GGUF/resolve/main/Lingshu-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill | + +Here is a handy graph by ikawrakow comparing some lower-quality quant +types (lower is better): + +![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) + +And here are Artefact2's thoughts on the matter: +https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 + +## FAQ / Model Request + +See https://huggingface.co/mradermacher/model_requests for some answers to +questions you might have and/or if you want some other model quantized. + +## Thanks + +I thank my company, [nethype GmbH](https://www.nethype.de/), for letting +me use its servers and providing upgrades to my workstation to enable +this work in my free time. + +