Instructions to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lovedheart/DeepSeek-V3.1-GGUF-IQ1_S",
	filename="IQ1_S/DeepSeek-V3.1-IQ1_S-00001-of-00030.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
# Run inference directly in the terminal:
llama-cli -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
# Run inference directly in the terminal:
llama-cli -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

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 lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

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 lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

Use Docker

docker model run hf.co/lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

LM Studio
Jan
Ollama
How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with Ollama:
```
ollama run hf.co/lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
```

Unsloth Studio

How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S 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 lovedheart/DeepSeek-V3.1-GGUF-IQ1_S 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 lovedheart/DeepSeek-V3.1-GGUF-IQ1_S to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lovedheart/DeepSeek-V3.1-GGUF-IQ1_S to start chatting

Atomic Chat new
Docker Model Runner
How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with Docker Model Runner:
```
docker model run hf.co/lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S
```

Lemonade

How to use lovedheart/DeepSeek-V3.1-GGUF-IQ1_S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lovedheart/DeepSeek-V3.1-GGUF-IQ1_S:IQ1_S

Run and chat with the model

lemonade run user.DeepSeek-V3.1-GGUF-IQ1_S-IQ1_S

List all available models

lemonade list

Based on Unsloth BF16 GGUF and imatrix file. The quantization is not programatically selected.
I carefully checked every detail of the imatrix statistics and obtain quantization suggestions from Qwen3-235B-A22B/DeepSeek V3.1/Gemini 2.5 Pro/ChatGPT.
Full protection of first 0-2 dense layers.
Full protection of output tensor and embedding layer.
Further compression is possible by llama.cpp.

Quantization details

--output-tensor-type BF16
--token-embedding-type BF16
--tensor-type attn_k_b=MXFP4 --tensor-type blk.[0|1|2|3|4].attn_k_b=BF16
--tensor-type attn_kv_a_mqa=Q4_K --tensor-type blk.[0|1|2].attn_kv_a_mqa=BF16
--tensor-type attn_output=IQ3_XXS --tensor-type blk.[0|1|2|3|4|5].attn_output=BF16 --tensor-type blk.58.attn_output=Q5_K --tensor-type blk.[59|60].attn_output=Q6_K
--tensor-type attn_q_a=Q4_K --tensor-type blk.[0|1|2].attn_q_a=BF16
--tensor-type attn_q_b=Q4_K --tensor-type blk.[0|1|2|3|4|5].attn_q_b=BF16 --tensor-type blk.6.attn_q_b=Q6_K
--tensor-type attn_v_b=Q6_K --tensor-type blk.[0|1|2].attn_v_b=BF16
--tensor-type blk.[0|1|2].ffn_down=BF16
--tensor-type blk.[0|1|2].ffn_up=BF16
--tensor-type blk.[0|1|2].ffn_gate=BF16
--tensor-type ffn_gate_exps=IQ1_S --tensor-type blk.[3|60].ffn_gate_exps=IQ2_XS
--tensor-type ffn_up_exps=IQ1_S --tensor-type blk.[3|60].ffn_up_exps=IQ2_XS
--tensor-type ffn_gate_shexp=Q6_K --tensor-type blk.[3|60].ffn_gate_shexp=BF16
--tensor-type ffn_up_shexp=Q6_K --tensor-type blk.[3|60].ffn_up_shexp=BF16
--tensor-type ffn_down_shexp=Q6_K --tensor-type blk.[3|60].ffn_down_shexp=BF16
--tensor-type ffn_down_exps=IQ1_S
--tensor-type blk.[3|4].ffn_down_exps=BF16
--tensor-type blk.[5|6|7|8|9|33|46|59|60].ffn_down_exps=MXFP4
--tensor-type blk.[25-38,40-45].ffn_down_exps=IQ2_XS
--tensor-type blk.39.ffn_down_exps=IQ2_S