Instructions to use FreeAIn/Pwen-3.5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FreeAIn/Pwen-3.5-2B 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 FreeAIn/Pwen-3.5-2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf FreeAIn/Pwen-3.5-2B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FreeAIn/Pwen-3.5-2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf FreeAIn/Pwen-3.5-2B: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 FreeAIn/Pwen-3.5-2B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FreeAIn/Pwen-3.5-2B: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 FreeAIn/Pwen-3.5-2B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FreeAIn/Pwen-3.5-2B:Q4_K_M
Use Docker
docker model run hf.co/FreeAIn/Pwen-3.5-2B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use FreeAIn/Pwen-3.5-2B with Ollama:
ollama run hf.co/FreeAIn/Pwen-3.5-2B:Q4_K_M
- Unsloth Studio
How to use FreeAIn/Pwen-3.5-2B 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 FreeAIn/Pwen-3.5-2B 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 FreeAIn/Pwen-3.5-2B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FreeAIn/Pwen-3.5-2B to start chatting
- Pi
How to use FreeAIn/Pwen-3.5-2B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FreeAIn/Pwen-3.5-2B: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": "FreeAIn/Pwen-3.5-2B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use FreeAIn/Pwen-3.5-2B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FreeAIn/Pwen-3.5-2B: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 "FreeAIn/Pwen-3.5-2B: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 FreeAIn/Pwen-3.5-2B with Docker Model Runner:
docker model run hf.co/FreeAIn/Pwen-3.5-2B:Q4_K_M
- Lemonade
How to use FreeAIn/Pwen-3.5-2B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FreeAIn/Pwen-3.5-2B:Q4_K_M
Run and chat with the model
lemonade run user.Pwen-3.5-2B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use FreeAIn/Pwen-3.5-2B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FreeAIn/Pwen-3.5-2B: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 FreeAIn/Pwen-3.5-2B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
| license: cc-by-sa-4.0 | |
| # Pwen 3.5 | |
| **Pwen 3.5** is a finetuned version of Qwen3.5-2B specialized for study notes, explanations, and concise technical summaries. Finetuned by Pavel Hanzel using Unsloth on Apple Silicon. | |
| ## Model Details | |
| | Attribute | Value | | |
| | --- | --- | | |
| | **Base Model** | `Qwen/Qwen3.5-2B` by Alibaba Cloud | | |
| | **Model Type** | Causal Language Model | | |
| | **Finetune Method** | QLoRA via Unsloth | | |
| | **Language** | English | | |
| | **License** | CC BY-SA 4.0 + Qwen License | | |
| | **Author** | Pavel Hanzel | | |
| | **Release Date** | July 2026 | | |
| ### Files & Sizes | |
| | File | Format | Size | Use Case | | |
| | --- | --- | --- | --- | | |
| | `Qwen3.5-2B.Q4_K_M.gguf` | GGUF Q4_K_M | ~1.3 GB | **Recommended** for Ollama, LM Studio, llama.cpp | | |
| ## Intended Use | |
| **Good for:** | |
| - Generating structured study notes with headers + bullets | |
| - Explaining code, math, and technical concepts concisely | |
| - Summarizing documents into revision format | |
| - Q&A in educational contexts | |
| **Not good for:** | |
| - Roleplay or creative fiction | |
| - Uncensored/unfiltered outputs - inherits Qwen safety | |
| - Non-English languages - trained on English only | |
| - Factual accuracy on events after 2025 | |
| ## Training Data | |
| Finetuned on 202 curated prompt-response pairs covering: | |
| 3. **Domains**: JavaScript, Python, algorithms, physics, general text | |
| LoRA config: `r=16`, `alpha=16`, `dropout=0.01`, target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`. Trained 300 Steps. | |
| ### Recommend not using thinking mode. It can break and stuck itself inside thinking loop! |