Instructions to use spicyneuron/GLM-5.1-MLX-2.9bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use spicyneuron/GLM-5.1-MLX-2.9bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("spicyneuron/GLM-5.1-MLX-2.9bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use spicyneuron/GLM-5.1-MLX-2.9bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "spicyneuron/GLM-5.1-MLX-2.9bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "spicyneuron/GLM-5.1-MLX-2.9bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use spicyneuron/GLM-5.1-MLX-2.9bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "spicyneuron/GLM-5.1-MLX-2.9bit"
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 spicyneuron/GLM-5.1-MLX-2.9bit
Run Hermes
hermes
- MLX LM
How to use spicyneuron/GLM-5.1-MLX-2.9bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "spicyneuron/GLM-5.1-MLX-2.9bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "spicyneuron/GLM-5.1-MLX-2.9bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "spicyneuron/GLM-5.1-MLX-2.9bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Update README.md
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README.md
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tags:
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- mlx
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---
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tags:
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- mlx
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---
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[GLM 5.1](https://huggingface.co/zai-org/GLM-5.1) optimized for MLX.
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# Usage
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```sh
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# Start server at http://localhost:8080/chat/completions
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uvx --from mlx-lm mlx_vlm.server \
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--host 127.0.0.1 \
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--port 8080 \
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--model spicyneuron/GLM-5.1-MLX-2.9bit
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```
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# Methodology
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Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
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MLX quantization options differ than llama.cpp, but the principles are the same:
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- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
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- More tolerant layers like MoE experts get lower precision
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# Benchmarks
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(WIP)
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Tested with:
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```
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mlx_lm.perplexity --sequence-length 2048 --seed 123
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mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
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mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
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mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
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mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000
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```
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