Instructions to use empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/Qwen3.8-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwen3.8-4B-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": "empero-ai/Qwen3.8-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

Ollama
How to use empero-ai/Qwen3.8-4B-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for empero-ai/Qwen3.8-4B-GGUF to start chatting

How to use empero-ai/Qwen3.8-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "empero-ai/Qwen3.8-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/Qwen3.8-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.8-4B-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwen3.8-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-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 empero-ai/Qwen3.8-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use empero-ai/Qwen3.8-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-4B-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 "empero-ai/Qwen3.8-4B-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"

Qwen3.8-4B — GGUF

Developed by Empero

GGUF quantizations of empero-ai/Qwen3.8-4B — a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-4B architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes.

This card is about choosing a file and running it. The capability writeup, full benchmark results, and best practices live on the main model card.

Headline results for the source model (CoT protocols, lm-evaluation-harness, identical settings base vs. student):

Task	Qwen3.5-4B (base)	Qwen3.8-4B	Δ
mmlu (CoT, 57 subjects)	0.354	0.553	+0.199
gsm8k_cot	0.850	0.785	−0.065

Qwen3.5-class models are hybrids: three Gated DeltaNet layers for every full-attention layer. A recent llama.cpp build with Qwen3.5 / Gated DeltaNet support is required — older builds will fail to load the architecture.

Files

File	Quant	Size	Notes
`Qwen3.8-4B-Q4_K_M.gguf`	Q4_K_M	2.783 GB	Recommended. Best quality/size balance for most users.
`Qwen3.8-4B-Q5_K_M.gguf`	Q5_K_M	3.161 GB	Higher quality at a modest size increase.
`Qwen3.8-4B-Q6_K.gguf`	Q6_K	3.563 GB	Near-lossless.
`Qwen3.8-4B-Q8_0.gguf`	Q8_0	4.611 GB	Highest-quality quantization.
`Qwen3.8-4B-BF16.gguf`	BF16	8.666 GB	Full precision reference.

Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).

What fits on a GPU?

Practical weight-size-based guidance at modest context — the KV cache is the dominant cost at long context and may require offload regardless of weight quant:

Quant	Guidance
Q4_K_M / Q5_K_M	Comfortable on 4–6 GB cards; strong CPU-only option as well.
Q6_K / Q8_0	6–8 GB recommended.
BF16	12 GB+.

Usage

llama.cpp

llama-cli -m Qwen3.8-4B-Q4_K_M.gguf \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -n 16384 -cnv

Use the built-in chat template (-cnv). The model is a reasoning model: every answer opens with a <think> block, so allow a generous -n and strip the <think>...</think> span for end users.

Ollama / LM Studio / Jan / KoboldCpp

Download the GGUF of your choice and load it directly; the chat template is embedded in the file. Recommended sampling: temperature=0.6, top_p=0.95, top_k=20.

Provenance & licensing

Quantizations of empero-ai/Qwen3.8-4B, a distillation of Qwen3.8 2.4T A95B into Qwen/Qwen3.5-4B trained on ~45,000 curated teacher traces from our internal Qwen3.8 distillation datasets. Weights are Apache-2.0, inherited from the Qwen base, shared as-is.

Stay in the loop

Support / Donate

If this model helped you, consider supporting the project:

BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

Acknowledgements

Developed and released by Empero
Base model: Qwen3.5-4B (Alibaba Qwen team)
GGUF quantization: llama.cpp (ggml-org)

Downloads last month: 2,527

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for empero-ai/Qwen3.8-4B-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

empero-ai/Qwen3.8-4B

Quantized

(1)

this model

Quantizations

1 model