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"

empero-ai commited on 2 days ago

Commit

391fc7d

0 Parent(s):

Qwen3.8 GGUF release

Browse files

Files changed (8) hide show

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+---
+license: apache-2.0
+base_model: empero-ai/Qwen3.8-4B
+base_model_relation: quantized
+language:
+  - en
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+  - gguf
+  - llama.cpp
+  - quantized
+  - empero-ai
+  - qwen3.5
+  - qwen3.8
+  - distillation
+  - reasoning
+  - gated-deltanet
+---
+# Qwen3.8-4B — GGUF
+**Developed by [Empero](https://empero.org)**
+GGUF quantizations of **[empero-ai/Qwen3.8-4B](https://huggingface.co/empero-ai/Qwen3.8-4B)** — a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-4B architecture — for [llama.cpp](https://github.com/ggml-org/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](https://huggingface.co/empero-ai/Qwen3.8-4B)**.
+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 |
+> [!Note]
+> 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
+```bash
+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](https://huggingface.co/empero-ai/Qwen3.8-4B)**, a distillation of Qwen3.8 2.4T A95B into [Qwen/Qwen3.5-4B](https://huggingface.co/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
+Sign up for the Empero newsletter at **[empero.org](https://empero.org)** for releases, evals, and research notes.
+## Support / Donate
+If this model helped you, consider supporting the project:
+- **BTC**: `bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v`
+- **LTC**: `ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x`
+## Acknowledgements
+- Developed and released by [Empero](https://empero.org)
+- Base model: [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Alibaba Qwen team)
+- GGUF quantization: [llama.cpp](https://github.com/ggml-org/llama.cpp) (ggml-org)

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