Instructions to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Solstice-AI/DeepSeek-V4.1-Flash-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 Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Solstice-AI/DeepSeek-V4.1-Flash-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 Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Solstice-AI/DeepSeek-V4.1-Flash-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 Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Solstice-AI/DeepSeek-V4.1-Flash-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 Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Solstice-AI/DeepSeek-V4.1-Flash-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": "Solstice-AI/DeepSeek-V4.1-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Ollama
How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with Ollama:
```
ollama run hf.co/Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M
```

Lemonade

How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-V4.1-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Solstice-AI/DeepSeek-V4.1-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Solstice-AI/DeepSeek-V4.1-Flash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

DeepSeek-V4.1-Flash GGUF

llama.cpp GGUF of deepseek-ai/DeepSeek-V4.1-Flash.

This is V4.1-Flash (DeepseekV41ForCausalLM), a causal decoder with engram n-gram lookup tables, hyper-connections and sparse attention. It is not V4-Flash-0731.

Recipe

How to build the engine, serve it, and the gotchas, plus the current status: vcruz305/DeepSeek-V4.1-Flash-GGUF-DGX-Spark-recipe

Status

These files do not run on upstream llama.cpp yet. Conversion works and is open as ggml-org/llama.cpp#28696. The runtime is in progress on the runtime/deepseek41 branch of vcruz305/llama.cpp: the loader, the engram tables and the hyper-connections work and are verified against the reference implementation, and the sparse attention is the remaining piece.

Weights land here as each rung finishes. Anything converted before 2026-09-10 carries general.architecture = deepseek4 and is being redone as deepseek41.

The architecture string is deepseek41, following llama.cpp's habit of dropping the _v (deepseek_v2 became deepseek2, deepseek_v3.2 became deepseek32).

2026-09-11 fix: the 4 Engram KV keys (head_count, key_length, max_ngram_size, layer_ids) were written with a hardcoded deepseek4.engram.* prefix instead of resolving {arch}.engram.* like every other arch-scoped key in the file. general.architecture and all 38 other arch-scoped keys were already correct (deepseek41.*); only these 4 were wrong, which would have made the runtime/deepseek41 loader fail to find Engram config on an otherwise loadable file. Fixed in place via a KV-only rewrite (tensor data untouched, verified byte-identical by SHA-256) on all five quant rungs' first shard, where GGUF split files store metadata. Confirmed live: all five now read deepseek41.engram.*.

Files

Ladder in order: Q2_K, Q3_K_M, Q4_K_M. Measured tensor payload:

File	Quant	Bytes	GiB
`DeepSeek-V4.1-Flash-Q2_K.gguf`	Q2_K	264,514,761,248	246.3
`DeepSeek-V4.1-Flash-Q3_K_M.gguf`	Q3_K_M	347,270,954,112	323.4
`DeepSeek-V4.1-Flash-Q4_K_M.gguf`	Q4_K_M	pending

Split into parts, since each exceeds the Hub's single file limit.

Q5_K_M is skipped unless asked for. The routed experts arrive as MXFP4 at 4.25 bpw, so higher rungs move parts of the mixture up rather than down: Q3_K_M already lands at 0.684 of the Q8_0 staging file, and Q5_K_M would be close enough to Q8_0 to be poor value.

Most of the file is the two engram tables, roughly 196.6B parameters between them. They follow the rung, 99,611 to 40,284 MiB each between q8_0 and q3_K.

Apache/MIT from upstream. Credit: DeepSeek. GGUF pack: Victor Cruz (vcruz305).

Downloads last month: 87

GGUF

Model size

748B params

Architecture

deepseek41

Hardware compatibility

3-bit

4-bit

8-bit

Model tree for Solstice-AI/DeepSeek-V4.1-Flash-GGUF

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