Instructions to use prithivMLmods/Smaug-Mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Smaug-Mini-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Smaug-Mini-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Smaug-Mini-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Smaug-Mini-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Smaug-Mini-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": "prithivMLmods/Smaug-Mini-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Smaug-Mini-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/Smaug-Mini-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Smaug-Mini-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/Smaug-Mini-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Smaug-Mini-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use prithivMLmods/Smaug-Mini-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/Smaug-Mini-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Smaug-Mini-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": "prithivMLmods/Smaug-Mini-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/Smaug-Mini-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Smaug-Mini-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Smaug-Mini-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-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 prithivMLmods/Smaug-Mini-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Smaug-Mini-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Smaug-Mini-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 "prithivMLmods/Smaug-Mini-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"
Smaug-Mini-GGUF
Smaug-Mini is Abacus.AI's agentic finetune of Qwen3.8-27B, trained via on-policy reinforcement learning (GRPO, LoRA-merged into the language trunk only, vision tower left bitwise-identical to the base) over multi-turn, tool-using automation episodes with verified, outcome-based rewards, targeting more reliable end-to-end tool use and automation performance while holding general capabilities at parity with the base model. It retains Qwen3.8-27B's architecture, layout, 262,144-token context, and
xhigh/medium/lowreasoning-effort interface exactly, functioning as a drop-in replacement, while delivering notable agentic gains — +4.5 on AutomationBench (41.8 vs. 37.3), +17.1 on JobBench (50.5 vs. 33.4), +13.5 on NL2Repo-Bench (55.8 vs. 42.3), and +2.0 overall on LiveBench (76.9 vs. 75.3) — alongside modest improvements on reasoning benchmarks like HLE and IFBench, with GPQA-diamond and MMMU-Pro held essentially at parity. Its key behavioral shift is redistributing deliberation rather than adding it: episodes finish about three steps sooner at roughly unchanged total reasoning volume, and episodes that exhaust their step budget without completing drop from 3.4% to 1.0%. It's served via vLLM with a Qwen3 reasoning parser and tool-call parser (recommended sampling: temperature 1.0, top_p 0.95, reasoning effortxhigh), with the inherited MTP head left untrained against the updated trunk — so speculative decoding via MTP should stay disabled — and is released under Apache 2.0, inherited from Qwen3.8-27B.
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| Smaug-Mini.BF16.gguf | BF16 | 53.8 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| Smaug-Mini.Q4_K_M.gguf | Q4_K_M | 16.5 GB | Link | Good quality, default size for most use cases, recommended. |
| Smaug-Mini.Q5_K_M.gguf | Q5_K_M | 19.2 GB | Link | High quality, recommended. |
| Smaug-Mini.mmproj-bf16.gguf | mmproj-bf16 | 931 MB | Link | Multimodal projection file in BF16 format. Used for vision/language models. |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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