Instructions to use davidflanagancomm/Wen 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 davidflanagancomm/Wen 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 davidflanagancomm/Wen:BF16 # Run inference directly in the terminal: llama cli -hf davidflanagancomm/Wen:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf davidflanagancomm/Wen:BF16 # Run inference directly in the terminal: llama cli -hf davidflanagancomm/Wen:BF16
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 davidflanagancomm/Wen:BF16 # Run inference directly in the terminal: ./llama-cli -hf davidflanagancomm/Wen:BF16
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 davidflanagancomm/Wen:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf davidflanagancomm/Wen:BF16
Use Docker
docker model run hf.co/davidflanagancomm/Wen:BF16
- LM Studio
- Jan
- Ollama
How to use davidflanagancomm/Wen with Ollama:
ollama run hf.co/davidflanagancomm/Wen:BF16
- Unsloth Desktop
- Pi
How to use davidflanagancomm/Wen with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf davidflanagancomm/Wen:BF16
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": "davidflanagancomm/Wen:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use davidflanagancomm/Wen with Docker Model Runner:
docker model run hf.co/davidflanagancomm/Wen:BF16
- Lemonade
How to use davidflanagancomm/Wen with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull davidflanagancomm/Wen:BF16
Run and chat with the model
lemonade run user.Wen-BF16
List all available models
lemonade list
- Hermes Agent
How to use davidflanagancomm/Wen with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf davidflanagancomm/Wen:BF16
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 davidflanagancomm/Wen:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use davidflanagancomm/Wen with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf davidflanagancomm/Wen:BF16
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 "davidflanagancomm/Wen:BF16" \ --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"
Wen 1.1
Experimental text/code checkpoint with a proof-of-concept BranchWrite LoRA merged into Wen's weights. Load Wen-1.1-BF16.gguf without an additional adapter.
This is a training-pipeline milestone. The small pilot did not demonstrate improved BranchWrite capability: the base and adapted models passed the same 15 of 19 checks, with the same four failures.
Model file
- File:
Wen-1.1-BF16.gguf - Size: 16,244,817,856 bytes
- SHA-256:
49425fd802b04b400a1725e8afe527c405bb15e828d0275ff295e38396839f34 - Precision: mostly BF16; the three merged matrices remain F32 to preserve small adapter updates. Other tensors retain their original BF16/F32 representation.
- Architecture metadata:
wen1. Use a runtime with Wen support, such as the updated Private_Context_COW.
Pilot and merge verification
The pilot used a rank-8, alpha-8 LoRA on the final layer's MLP gate, up and down projections, with nine mixed training examples and four optimizer steps. Held-out and preservation checks were evaluated separately.
The merged checkpoint was compared against the original checkpoint loaded with the adapter in native COW. All 132 tested next-token choices matched, including the 128-token greedy continuation after a 1,024-token prefix. The largest measured absolute logit difference was approximately 0.0000062. This verifies the merge on those probes; it does not establish a task-quality improvement.
The other 424 tensors were preserved byte for byte. The original checkpoint and adapter remain available locally; the old model file has been removed from the current repository tree.
- Downloads last month
- 33
16-bit