--- base_model: raincandy-u/Llama-3-5B-Sheard datasets: - JeanKaddour/minipile - raincandy-u/SlimOrca-Llama-3-Preference-DPO-Pairs language: - en library_name: transformers license: other license_link: LICENSE license_name: llama3 quantized_by: mradermacher tags: - facebook - meta - pytorch - llama - llama-3 --- ## About static quants of https://huggingface.co/raincandy-u/Llama-3-5B-Sheard weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-5B-Sheard-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/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q2_K.gguf) | Q2_K | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.IQ3_XS.gguf) | IQ3_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q3_K_S.gguf) | Q3_K_S | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.IQ3_S.gguf) | IQ3_S | 2.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.IQ3_M.gguf) | IQ3_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q3_K_M.gguf) | Q3_K_M | 3.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q3_K_L.gguf) | Q3_K_L | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.IQ4_XS.gguf) | IQ4_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q4_K_S.gguf) | Q4_K_S | 3.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q4_K_M.gguf) | Q4_K_M | 3.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q5_K_S.gguf) | Q5_K_S | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q5_K_M.gguf) | Q5_K_M | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q6_K.gguf) | Q6_K | 4.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.Q8_0.gguf) | Q8_0 | 6.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-5B-Sheard-GGUF/resolve/main/Llama-3-5B-Sheard.f16.gguf) | f16 | 11.8 | 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.