Instructions to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Tankie-DPE-12b-SFT-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Tankie-DPE-12b-SFT-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Tankie-DPE-12b-SFT-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tankie-DPE-12b-SFT-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

mradermacher commited on Nov 11, 2025

Commit

942c35d

verified ·

1 Parent(s): 117ecfb

auto-patch README.md

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@@ -1,3 +1,20 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +24,51 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/WokeAI/Tankie-DPE-12b-SFT

+---
+base_model: WokeAI/Tankie-DPE-12b-SFT
+datasets:
+- WokeAI/polititune-tankie-warmup
+language:
+- en
+library_name: transformers
+license: apache-2.0
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- character-training
+- communism
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/WokeAI/Tankie-DPE-12b-SFT
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Tankie-DPE-12b-SFT-i1-GGUF).***
+static quants are available at https://huggingface.co/mradermacher/Tankie-DPE-12b-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/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
+| [GGUF](https://huggingface.co/mradermacher/Tankie-DPE-12b-SFT-i1-GGUF/resolve/main/Tankie-DPE-12b-SFT.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
+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.
+<!-- end -->