Instructions to use strykes/SteraVibeThinker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use strykes/SteraVibeThinker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="strykes/SteraVibeThinker") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("strykes/SteraVibeThinker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use strykes/SteraVibeThinker 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 strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: llama cli -hf strykes/SteraVibeThinker:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: llama cli -hf strykes/SteraVibeThinker: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 strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf strykes/SteraVibeThinker: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 strykes/SteraVibeThinker:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf strykes/SteraVibeThinker:Q4_K_M
Use Docker
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use strykes/SteraVibeThinker with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "strykes/SteraVibeThinker" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "strykes/SteraVibeThinker", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- SGLang
How to use strykes/SteraVibeThinker 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 "strykes/SteraVibeThinker" \ --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": "strykes/SteraVibeThinker", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "strykes/SteraVibeThinker" \ --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": "strykes/SteraVibeThinker", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use strykes/SteraVibeThinker with Ollama:
ollama run hf.co/strykes/SteraVibeThinker:Q4_K_M
- Unsloth Studio
How to use strykes/SteraVibeThinker 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 strykes/SteraVibeThinker 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 strykes/SteraVibeThinker to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for strykes/SteraVibeThinker to start chatting
- Pi
How to use strykes/SteraVibeThinker with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker: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": "strykes/SteraVibeThinker:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use strykes/SteraVibeThinker with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker: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 strykes/SteraVibeThinker:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use strykes/SteraVibeThinker with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf strykes/SteraVibeThinker: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 "strykes/SteraVibeThinker: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"
- Docker Model Runner
How to use strykes/SteraVibeThinker with Docker Model Runner:
docker model run hf.co/strykes/SteraVibeThinker:Q4_K_M
- Lemonade
How to use strykes/SteraVibeThinker with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull strykes/SteraVibeThinker:Q4_K_M
Run and chat with the model
lemonade run user.SteraVibeThinker-Q4_K_M
List all available models
lemonade list
| license: mit | |
| base_model: WeiboAI/VibeThinker-3B | |
| tags: | |
| - code | |
| - agent | |
| - tool-use | |
| - gguf | |
| - qwen2 | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| # SteraVibeThinker | |
| A full fine-tune of [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) | |
| (a 3B reasoning model built on the Qwen2.5-3B / Qwen2.5-Coder-3B architecture) on | |
| the ~30k-example **Tiny-Giant** agentic tool-use dataset. | |
| The goal: keep VibeThinker's strong verifiable-reasoning core while teaching it the | |
| deterministic, Hermes/ChatML-style `<tool_call>` agent format used by the | |
| Tiny-Giant harness. | |
| ## Files | |
| | File | Description | | |
| |---|---| | |
| | `SteraVibeThinker-Q4_K_M.gguf` | Q4_K_M quantization (~1.8 GB) — for `llama.cpp` / Ollama / LM Studio | | |
| | `SteraVibeThinker-f16.gguf` | f16 GGUF (~5.8 GB) — re-quantize to any level without retraining | | |
| | `raw_weights/` | Full bf16 safetensors HF checkpoint | | |
| | `val_meta.jsonl` | Held-out validation set shipped with the model | | |
| ## Training | |
| - **Base:** `WeiboAI/VibeThinker-3B` (MIT, Qwen2.5-3B architecture, ChatML-native) | |
| - **Method:** full fine-tune (not LoRA), bf16 + gradient checkpointing | |
| - **Data:** ~30k Tiny-Giant agentic tool-use conversations | |
| - **Epochs:** 2 · **LR:** 7e-6 (cosine, 3% warmup) · **Seq len:** 4096 | |
| - **Loss:** full-sequence (tool results modeled as in-distribution context) | |
| ## Prompt format | |
| This model was trained with an **explicit ChatML / Hermes renderer**, not | |
| `tokenizer.apply_chat_template`. Pin the ChatML template explicitly when serving — | |
| do not rely on auto-detection. Tool calls use: | |
| ``` | |
| <tool_call> | |
| {"name": "<function-name>", "arguments": {...}} | |
| </tool_call> | |
| ``` | |
| ## Inference (llama.cpp) | |
| ```bash | |
| llama-cli -m SteraVibeThinker-Q4_K_M.gguf --chat-template chatml | |
| ``` | |
| ## License | |
| MIT, inherited from the VibeThinker-3B base model. | |