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

Pi new

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 27

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713e080

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@@ -30,27 +30,32 @@ uvx --from mlx-lm mlx_lm.server \
 # 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),

 # Benchmarks
+metric | baa-ai/GLM-5.1-RAM-270GB-MLX | 2.9 bit (this model) | 3.6 bit
+--- | --- | --- | ---
+bpw | 3.110 | 2.906 | 3.645
+base memory | 269.303 | 251.702 | 315.648
+peak memory (1024/512) | 291.257 | 272.358 | 341.020
+prompt tok/s (1024) | 194.958 ± 0.075 | 194.216 ± 0.167 | 190.508 ± 0.880
+gen tok/s (512) | 21.381 ± 0.050 | 19.527 ± 0.035 | 17.873 ± 0.156
+kl mean | 0.686 ± 0.054 | 0.268 ± 0.009 | 0.117 ± 0.004
+kl p95 | 1.478 ± 0.054 | 0.537 ± 0.009 | 0.236 ± 0.004
+perplexity | 4.780 ± 0.020 | 4.118 ± 0.016 | 3.945 ± 0.016
+piqa | 0.776 ± 0.010 | 0.794 ± 0.009 | 0.820 ± 0.017
 Tested on a Mac Studio M3 Ultra with:
 ```
+mlx_lm.kld --baseline-model path/to/mlx-full-precision
 mlx_lm.perplexity --sequence-length 2048 --seed 123
 mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
+mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 500
 ```
+Note:
+- `mlx_lm.kld` is approximate, based on `top_k` not full logits. Here's the [code](https://github.com/ml-explore/mlx-lm/pull/1146).
+- GLM 5.1 KL divergence calculated against the largest quant I could run locally (~495 GB), so real KL is higher.
 # Methodology
 Quantized with a [mlx-lm fork](https://github.com/ml-explore/mlx-lm/pull/922),