Instructions to use spicyneuron/GLM-5.1-MLX-3.6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/GLM-5.1-MLX-3.6bit 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-3.6bit")

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

How to use spicyneuron/GLM-5.1-MLX-3.6bit 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-3.6bit"

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-3.6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/GLM-5.1-MLX-3.6bit 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-3.6bit"

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-3.6bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/GLM-5.1-MLX-3.6bit 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-3.6bit"

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-3.6bit"
# 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-3.6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

spicyneuron commited on Apr 26

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+---
+language:
+- en
+- zh
+library_name: mlx
+license: mit
+pipeline_tag: text-generation
+base_model: zai-org/GLM-5.1
+tags:
+- mlx
+---
+(Uploading...)
+[GLM 5.1](https://huggingface.co/zai-org/GLM-5.1) optimized to run _comfortably_
+on a Mac Studio M3 512. This is the quality-first version. Smaller, compact
+version [here](https://huggingface.co/spicyneuron/GLM-5.1-MLX-2.9bit).
+- A mixed-precision quant that balances speed, memory, and accuracy.
+- 3-bit baseline with important layers at 4, 8 and BF16.
+- Fits into ~350 GB memory, leaving plenty of room to run parallel models (ex: Minimax M2.7, Qwen 3.6 35B).
+# Usage
+```sh
+# Start server at http://localhost:8080/chat/completions
+uvx --from mlx-lm mlx_lm.server \
+  --host 127.0.0.1 \
+  --port 8080 \
+  --model spicyneuron/GLM-5.1-MLX-3.6bit
+```
+# Benchmarks
+metric | baa-ai/GLM-5.1-RAM-270GB-MLX | 2.9bit
+--- | --- | ---
+bpw | 3.1096 | 2.9064
+peak memory (1024/512) | 291.257 | 272.358
+prompt tok/s (1024) | 194.958 ± 0.075 | 194.216 ± 0.167
+gen tok/s (512) | 21.381 ± 0.050 | 19.527 ± 0.035
+perplexity | 4.780 ± 0.020 | 4.118 ± 0.016
+hellaswag | 0.546 ± 0.011 | 0.59 ± 0.011
+piqa | 0.776 ± 0.01 | 0.794 ± 0.009
+winogrande | 0.668 ± 0.013 | 0.695 ± 0.013
+Tested on a Mac Studio M3 Ultra with:
+```
+mlx_lm.perplexity --sequence-length 2048 --seed 123
+mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
+mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
+mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
+mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000
+```
+# Methodology
+Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922),
+drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
+MLX quantization options differ from llama.cpp, but the principles are the
+same:
+- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
+- More tolerant layers like MoE experts get lower precision