| --- |
| base_model: EleutherAI/pythia-2.8b-deduped |
| datasets: |
| - EleutherAI/the_pile_deduplicated |
| language: |
| - en |
| library_name: transformers |
| license: apache-2.0 |
| quantized_by: mradermacher |
| tags: |
| - pytorch |
| - causal-lm |
| - pythia |
| --- |
| ## About |
|
|
| <!-- ### quantize_version: 2 --> |
| <!-- ### output_tensor_quantised: 1 --> |
| <!-- ### convert_type: hf --> |
| <!-- ### vocab_type: --> |
| <!-- ### tags: --> |
| static quants of https://huggingface.co/EleutherAI/pythia-2.8b-deduped |
| |
| <!-- provided-files --> |
| weighted/imatrix quants are available at https://huggingface.co/mradermacher/pythia-2.8b-deduped-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/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q2_K.gguf) | Q2_K | 1.2 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.IQ3_XS.gguf) | IQ3_XS | 1.3 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.IQ3_S.gguf) | IQ3_S | 1.3 | beats Q3_K* | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q3_K_S.gguf) | Q3_K_S | 1.3 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.IQ3_M.gguf) | IQ3_M | 1.5 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q3_K_M.gguf) | Q3_K_M | 1.6 | lower quality | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.IQ4_XS.gguf) | IQ4_XS | 1.6 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q3_K_L.gguf) | Q3_K_L | 1.7 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q4_K_M.gguf) | Q4_K_M | 1.9 | fast, recommended | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q5_K_S.gguf) | Q5_K_S | 2.0 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q5_K_M.gguf) | Q5_K_M | 2.2 | | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q6_K.gguf) | Q6_K | 2.4 | very good quality | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality | |
| | [GGUF](https://huggingface.co/mradermacher/pythia-2.8b-deduped-GGUF/resolve/main/pythia-2.8b-deduped.f16.gguf) | f16 | 5.7 | 16 bpw, overkill | |
| |
| Here is a handy graph by ikawrakow comparing some lower-quality quant |
| types (lower is better): |
| |
|  |
| |
| 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. |
|
|
| <!-- end --> |
|
|
