Instructions to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/DeepSeek-V3.2-Speciale-GGUF",
	filename="IQ3_K/DeepSeek-V3.2-Speciale-IQ3_K-00001-of-00008.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/DeepSeek-V3.2-Speciale-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 ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

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 ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

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 ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/DeepSeek-V3.2-Speciale-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/DeepSeek-V3.2-Speciale-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

Ollama
How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with Ollama:
```
ollama run hf.co/ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/DeepSeek-V3.2-Speciale-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/DeepSeek-V3.2-Speciale-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/DeepSeek-V3.2-Speciale-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K
```

Lemonade

How to use ubergarm/DeepSeek-V3.2-Speciale-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/DeepSeek-V3.2-Speciale-GGUF:Q2_K

Run and chat with the model

lemonade run user.DeepSeek-V3.2-Speciale-GGUF-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of deepseek-ai/DeepSeek-V3.2-Speciale

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8.

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw.

Tis one is just a test quants for baseline perplexity comparison:

Q8_0 664.295 GiB (8.504 BPW)
- Final estimate: PPL over 561 chunks for n_ctx=512 = 3.2082 +/- 0.01742

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

IQ5_K 464.467 GiB (5.946 BPW)

Final estimate: PPL over 561 chunks for n_ctx=512 = 3.2122 +/- 0.01744

👈 Secret Recipe

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
# attn_kv_b is only used for PP so keep it q8_0 for best speed and accuracy
blk\..*\.attn_kv_b\.weight=q8_0

# ideally k_b and v_b are smaller than q8_0 as they are is used for TG with -mla 3
# https://github.com/ikawrakow/ik_llama.cpp/issues/651
# blk.*.attn_k_b.weight is not divisible by 256 so only supports iq4_nl or legacy qN_0
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0

## First Three Dense Layers [0-2] (GPU)
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

## Shared Expert (1-60) (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

## Routed Experts (1-60) (CPU)
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

## Token embedding and output tensors (GPU)
token_embd\.weight=q8_0
output\.weight=q8_0
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/imatrix-DeepSeek-V3.2-Speciale-Q8_0.dat \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-256x20B-safetensors-BF16-00001-of-00030.gguf \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-IQ5_K.gguf \
    IQ5_K \
    128

IQ3_K 290.897 GiB (3.724 BPW)

Final estimate: PPL over 561 chunks for n_ctx=512 = 3.2793 +/- 0.01789

👈 Secret Recipe

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=iq6_k

## First Three Dense Layers [0-2] (GPU)
blk\.0\.ffn_down\.weight=q8_0
blk\.0\.ffn_(gate|up)\.weight=q8_0
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=iq6_k

## Shared Expert [3-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k

## Routed Experts [3-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq4_kss
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/imatrix-DeepSeek-V3.2-Speciale-Q8_0.dat \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-256x20B-safetensors-BF16-00001-of-00030.gguf \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-IQ3_K.gguf \
    IQ3_K \
    128

smol-IQ2_KS 179.444 GiB (2.297 BPW)

Final estimate: PPL over 561 chunks for n_ctx=512 = 3.8950 +/- 0.02237

👈 Secret Recipe

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0

# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

## First Three Dense Layers [0-2] (GPU)
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_kss

## Shared Expert [3-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq4_kss

## Routed Experts [3-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq2_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/imatrix-DeepSeek-V3.2-Speciale-Q8_0.dat \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-256x20B-safetensors-BF16-00001-of-00030.gguf \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

smol-IQ1_KT 146.165 GiB (1.871 BPW)

Final estimate: PPL over 561 chunks for n_ctx=512 = 4.3623 +/- 0.02590

👈 Secret Recipe

#!/usr/bin/env bash

custom="
## Attention [0-60] (GPU)
blk\..*\.attn_k_b\.weight=q6_0
blk\..*\.attn_v_b\.weight=iq6_k

# Balance of attn tensors
blk\..*\.attn_kv_a_mqa\.weight=iq6_k
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

## First Three Dense Layers [0-2] (GPU)
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_kss

## Shared Expert [3-60] (GPU)
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq4_kss

## Routed Experts [3-60] (CPU)
blk\..*\.ffn_down_exps\.weight=iq1_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

## Token embedding and output tensors (GPU)
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/imatrix-DeepSeek-V3.2-Speciale-Q8_0.dat \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-256x20B-safetensors-BF16-00001-of-00030.gguf \
    /mnt/data/models/ubergarm/DeepSeek-V3.2-Speciale-GGUF/DeepSeek-V3.2-Speciale-smol-IQ1_KT.gguf \
    IQ1_KT \
    128

Quick Start

NOTE 1: This quant has ripped out the sparse attention lightning index tensors. See sszymczyk/DeepSeek-V3.2-nolight-GGUF

NOTE 2: This model doesn't do tool calling:

Please note that the DeepSeek-V3.2-Speciale variant is designed exclusively for deep reasoning tasks and does not support the tool-calling functionality. https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale#how-to-run-locally

NOTE 3: The original model card has chat template discussions. These GGUFs have baked in the modified pervious Exp November release version which seems to work okay thanks to: sszymczyk

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

# Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

# Run Hybrid CPU + 2x CUDA GPUs (48GB VRAM each older RTX A6000 non-PROs)
## no -sm graph for DeepSeek yet. Is there an easy way to disable thinking or need to prefill response?
## might be better way to do this with --n-cpu-moe 46 -ts 48,48 etc...
./build/bin/llama-server \
    --model "$model" \
    --alias ubergarm/DeepSeek-V3.2-Speciale-GGUF \
    --ctx-size 32768 \
    -ctk q8_0 \
    -ger \
    --merge-qkv \
    -mla 3 -amb 1024 \
    -ot "blk\.(3|4|5|6|7|8|9|10)\.ffn_(gate|up|down)_exps.*=CUDA0" \
    -ot "blk\.(52|53|54|55|56|57|58|59|60)\.ffn_(gate|up|down)_exps.*=CUDA1" \
    --cpu-moe \
    -ub 4096 -b 4096 \
    --threads 24 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

# CPU Only
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/DeepSeek-V3.2-Speciale-GGUF \
    --merge-qkv \
    --ctx-size 131072 \
    -ctk q8_0 \
    -mla 3 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

    # --validate-quants

You can always bring your own template with --chat-template-file myTemplate.jinja and might need --special etc.

References

Downloads last month: 95

GGUF

Model size

671B params

Architecture

deepseek2

Hardware compatibility

2-bit

View +1 variant

Model tree for ubergarm/DeepSeek-V3.2-Speciale-GGUF

Base model

deepseek-ai/DeepSeek-V3.2-Exp-Base

Finetuned

deepseek-ai/DeepSeek-V3.2-Speciale

Quantized

(3)

this model

ik_llama.cpp imatrix Quantizations of deepseek-ai/DeepSeek-V3.2-Speciale

Big Thanks

Quant Collection

IQ5_K 464.467 GiB (5.946 BPW)

IQ3_K 290.897 GiB (3.724 BPW)

smol-IQ2_KS 179.444 GiB (2.297 BPW)

smol-IQ1_KT 146.165 GiB (1.871 BPW)

Quick Start

References

Model tree for ubergarm/DeepSeek-V3.2-Speciale-GGUF

`ik_llama.cpp` imatrix Quantizations of deepseek-ai/DeepSeek-V3.2-Speciale