Instructions to use avlp12/GLM-5.3-Flash-Alis-MLX-8bit 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-8bit 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-8bit") 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-8bit 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-8bit"
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-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use avlp12/GLM-5.3-Flash-Alis-MLX-8bit 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-8bit"
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-8bit" # 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-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use avlp12/GLM-5.3-Flash-Alis-MLX-8bit 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-8bit"
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-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use avlp12/GLM-5.3-Flash-Alis-MLX-8bit 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-8bit"
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-8bit" \ --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-8bit
MLX (mlx-vlm tree) 8-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). 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
(standalone drafter: avlp12/GLM-5.3-Flash-Alis-MTP-Drafter). Conversion receipts (state.json,
finalize_receipt.json) are in-repo. 334.1 GB on disk.
Recipe
8-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.
Role in the family
This is the highest-fidelity tier of the family and the teacher/reference class used to score
the 4-bit builds (held-out paired KL): 4-bit QUASAR avlp12/GLM-5.3-Flash-Alis-MLX-4bit measures
−8.5% KL vs its 4-bit RTN baseline against an 8-bit teacher of this layout family (6-bit measures
≈0.0226 mean KL on the same panel). Expect ≈24-25 tok/s decode @ p512 on an M3 Ultra 512GB
(8-bit class), vs ≈29-33 tok/s for the 4-bit build; 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.
Runtime
Requires mlx-vlm with glm5_next (merged 2026-08-26); text-stack correctness fixes live in the
PR #2044/#2074
branches — recommended until merged.
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8-bit
Model tree for avlp12/GLM-5.3-Flash-Alis-MLX-8bit
Base model
zai-org/GLM-5.3-Flash