Instructions to use avlp12/GLM-5.3-Flash-Alis-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avlp12/GLM-5.3-Flash-Alis-MLX-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("avlp12/GLM-5.3-Flash-Alis-MLX-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 Settings
- LM Studio
- Pi
How to use avlp12/GLM-5.3-Flash-Alis-MLX-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 "avlp12/GLM-5.3-Flash-Alis-MLX-6bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "avlp12/GLM-5.3-Flash-Alis-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use avlp12/GLM-5.3-Flash-Alis-MLX-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 "avlp12/GLM-5.3-Flash-Alis-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "avlp12/GLM-5.3-Flash-Alis-MLX-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "avlp12/GLM-5.3-Flash-Alis-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use avlp12/GLM-5.3-Flash-Alis-MLX-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 "avlp12/GLM-5.3-Flash-Alis-MLX-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 avlp12/GLM-5.3-Flash-Alis-MLX-6bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use avlp12/GLM-5.3-Flash-Alis-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "avlp12/GLM-5.3-Flash-Alis-MLX-6bit"
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 "avlp12/GLM-5.3-Flash-Alis-MLX-6bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
GLM-5.3-Flash-Alis-MLX-6bit
MLX (mlx-vlm tree) 6-bit build of GLM-5.3-Flash
(320B-A18B, glm5_next), converted by streaming dequant of the official FP8 release (84c6a6aa).
This replaces the withdrawn earlier build (which quantized the MoE router and decoded at
≈5.5 tok/s). This build keeps the router (mlp.gate) and correction bias unquantized/fp32,
the mHC arrays and KDA A_log/dt_bias in fp32 as stored, and the vision tower in bf16;
the MTP layer (45) is dropped (a standalone MTP drafter is published separately as
avlp12/GLM-5.3-Flash-Alis-MTP-Drafter). Conversion receipts (state.json,
finalize_receipt.json) are in-repo.
Recipe
6-bit g64 affine on experts, attention, dense/shared MLPs, embeddings and head; router / mHC /
KDA decay params / norms / convs as stored; vision bf16. Per-module map in config.json.
Measured (M3 Ultra 512GB)
| prompt | decode tok/s (oMLX SSE) |
|---|---|
| p512 | 26.3 |
| p2048 | 25.3 |
| p4096 | 24.6 |
Held-out paired KL vs the 8-bit teacher of this family (en/ko/code, ctx-2048, same harness as
the 4-bit builds): 0.0269 / 0.0226 / 0.0177, weighted mean ≈ 0.0226 — versus 0.0569 for the
4-bit QUASAR build and 0.0622 for 4-bit RTN. Same comparability caveat as the 4-bit card:
quantized-teacher, private panel — not comparable to BF16-teacher KLD numbers elsewhere.
Add MLX_MAX_MB_PER_BUFFER=2048 MLX_MAX_OPS_PER_BUFFER=100000 to the serving environment for
≈+12% decode on M3 Ultra.
The fused-KDA Metal kernel and long-context gathered-prefill findings documented on the
4bit card (+20% decode, +50% prefill
at 131k, both bit-identical) are architecture-level and apply to this build too; measured on the
4-bit tree.
For the fastest 4-bit-class build of this family (QUASAR-init, held-out KL −8.5% vs 4-bit RTN),
see avlp12/GLM-5.3-Flash-Alis-MLX-4bit.
Runtime
Requires mlx-vlm with glm5_next (merged 2026-08-26); the text-stack correctness fixes
(swiglu clamp, fp32 mHC/KDA aux, eps) live in the PR
#2044/#2074
branches — recommended until merged.
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6-bit
Model tree for avlp12/GLM-5.3-Flash-Alis-MLX-6bit
Base model
zai-org/GLM-5.3-Flash