---
base_model: ubitech-edg/commandr-35b-cpt-sft
datasets:
- arxiv
- gov
- news
- wikipedia
- axolotl_deduplicated_synthetic_qa
language:
- en
library_name: transformers
license: apache-2.0
model_name: commandr-35b-cpt-sft
model_type: AutoModelForCausalLM
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation
- causal-lm
- two-stage-training
- continual-pretraining
- supervised-fine-tuning
- synthetic-qa
- lora
- axolotl
- deepspeed
- transformers
- commandr
- cohere
- eu-hpc
---
## About

<!-- ### quantize_version: 2 -->
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<!-- ### quants:  Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
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weighted/imatrix quants of https://huggingface.co/ubitech-edg/commandr-35b-cpt-sft

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#commandr-35b-cpt-sft-i1-GGUF).***

static quants are available at https://huggingface.co/mradermacher/commandr-35b-cpt-sft-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/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ1_S.gguf) | i1-IQ1_S | 8.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ1_M.gguf) | i1-IQ1_M | 9.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.3 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.2 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_S.gguf) | i1-IQ2_S | 11.9 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ2_M.gguf) | i1-IQ2_M | 12.8 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q2_K_S.gguf) | i1-Q2_K_S | 12.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q2_K.gguf) | i1-Q2_K | 13.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_XS.gguf) | i1-IQ3_XS | 15.2 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_S.gguf) | i1-IQ3_S | 16.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_S.gguf) | i1-Q3_K_S | 16.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ3_M.gguf) | i1-IQ3_M | 16.8 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q3_K_L.gguf) | i1-Q3_K_L | 19.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-IQ4_XS.gguf) | i1-IQ4_XS | 19.3 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_0.gguf) | i1-Q4_0 | 20.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q4_1.gguf) | i1-Q4_1 | 22.4 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.4 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.1 |  |
| [GGUF](https://huggingface.co/mradermacher/commandr-35b-cpt-sft-i1-GGUF/resolve/main/commandr-35b-cpt-sft.i1-Q6_K.gguf) | i1-Q6_K | 28.8 | practically like static Q6_K |

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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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