--- base_model: HiTZ/latxa-7b-v1.2 datasets: - HiTZ/latxa-corpus-v1.1 language: - eu - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About static quants of https://huggingface.co./HiTZ/latxa-7b-v1.2 weighted/imatrix quants are available at https://huggingface.co./mradermacher/latxa-7b-v1.2-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/latxa-7b-v1.2-GGUF/resolve/main/.IQ3_XS.gguf) | IQ3_XS | 2.9 | | | [GGUF](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q2_K.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q2_K.gguf) | Q2_K | 5.2 | | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.IQ3_S.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.IQ3_S.gguf) | IQ3_S | 6.0 | beats Q3_K* | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q3_K_S.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q3_K_S.gguf) | Q3_K_S | 6.0 | | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.IQ3_M.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.IQ3_M.gguf) | IQ3_M | 6.3 | | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q3_K_M.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q3_K_M.gguf) | Q3_K_M | 6.7 | lower quality | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q4_K_S.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q4_K_S.gguf) | Q4_K_S | 7.8 | fast, recommended | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q6_K.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q6_K.gguf) | Q6_K | 11.2 | very good quality | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.Q8_0.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.Q8_0.gguf) | Q8_0 | 14.4 | fast, best quality | | [PART 1](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/.f16.gguf) [PART 2](https://huggingface.co./mradermacher/latxa-7b-v1.2-GGUF/resolve/main/latxa-7b-v1.2.f16.gguf) | f16 | 27.1 | 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.