Instructions to use JANGQ-AI/GLM-5.2-JANG_2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/GLM-5.2-JANG_2L 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("JANGQ-AI/GLM-5.2-JANG_2L")

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

How to use JANGQ-AI/GLM-5.2-JANG_2L with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/GLM-5.2-JANG_2L"

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": "JANGQ-AI/GLM-5.2-JANG_2L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JANGQ-AI/GLM-5.2-JANG_2L 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 "JANGQ-AI/GLM-5.2-JANG_2L"

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 JANGQ-AI/GLM-5.2-JANG_2L

Run Hermes

hermes

OpenClaw new

How to use JANGQ-AI/GLM-5.2-JANG_2L with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/GLM-5.2-JANG_2L"

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 "JANGQ-AI/GLM-5.2-JANG_2L" \
  --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"

MLX LM

How to use JANGQ-AI/GLM-5.2-JANG_2L with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "JANGQ-AI/GLM-5.2-JANG_2L"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "JANGQ-AI/GLM-5.2-JANG_2L"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "JANGQ-AI/GLM-5.2-JANG_2L",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Access is manual — please read the warning first

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

There is no working runtime for this bundle. vMLX, Osaurus, MLX Studio and stock mlx_lm cannot load it today. It is a 318 GB research artifact published for archival and for researchers working on SSD-offloaded MoE inference. It will not fit in RAM on any shipping Apple Silicon machine, and there is no supported way to generate text with it. Requests are reviewed manually — please say what you intend to do with it.

⚠️ NO RUNTIME AVAILABLE YET — DO NOT EXPECT THIS TO RUN

There is no vMLX / Osaurus / MLX Studio runtime for this bundle. Stock mlx_lm will not load it either.

These weights are published as an archival research artifact, not as a usable model. At 318 GB they do not fit in RAM on any shipping Apple Silicon machine, and the only code that has ever produced coherent text from them is an unreleased SSD-offload prototype (mlxpress) that tops out at ~2.8 tok/s and was shelved for missing its performance bar.

If you want a GLM MoE bundle you can actually run today, use GLM-5.1-JANG_1L instead.

GLM-5.2-JANG_2L

745B-parameter Mixture-of-Experts quantized to 3.12 bits/weight — 318 GB Created by Jinho Jang — eric@jangq.ai

A JANG v2 affine mixed-precision quantization of zai-org/GLM-5.2, produced 2026-07-19 with jang_tools convert -p JANG_2L. The conversion is complete and structurally verified; what does not exist is a runtime that can serve it.

Why this is here

This bundle was built for the mlxpress campaign — an attempt to run a 745B MoE on a 128 GB Apple Silicon machine by keeping only hot experts resident and streaming cold experts from SSD. The quantization worked. The offload engine worked and produced coherent long-context output. It was too slow to ship (~2.8 tok/s against a 6 tok/s bar), so the campaign was paused and no runtime was ever released.

Rather than delete a 318 GB artifact that took a 1.5 TB download and a multi-hour conversion to produce, it is archived here. It is useful if you are working on SSD-offloaded MoE inference, expert-routing analysis, or low-bit quantization of DeepSeek-V3.2-family architectures.

Model summary

Field	Value
Base model	`zai-org/GLM-5.2` (bf16 source, not FP8)
Architecture	`GlmMoeDsaForCausalLM` / `model_type=glm_moe_dsa` (DeepSeek-V3.2 family + DSA sparse attention)
Parameters	~745 B total, ~40 B active per token
Layers	78 transformer layers + layer 78 = MTP (MTP is present in this bundle, not stripped)
Routed experts	256, top-8, sigmoid routing (`noaux_tc`), `routed_scaling_factor=2.5`
Shared experts	1
Dense layers	first 3 (`first_k_dense_replace=3`), `intermediate_size=12288`
Expert MLP	`moe_intermediate_size=2048`
Hidden size	6144
Attention	MLA — `q_lora_rank=2048`, `kv_lora_rank=512`, 64 heads, `qk_head_dim=256`, `v_head_dim=256`
DSA indexer	32 heads, `index_head_dim=128`, `index_topk=2048`, `index_topk_freq=4`
RoPE	`theta=8e6`, interleaved
Context window	1,048,576 (1M) per config
Vocab	154,880
EOS tokens	`[154820, 154827, 154829]`
Format	JANG v2 — MLX-native safetensors, mmap load
On-disk size	318 GiB (341,207,533,424 bytes) across 233 shards, 3,303 tensors
Mode	Text-only

Quantization

Profile JANG_2L, affine, via mx.quantize. No AWQ, no Hadamard rotation, no expert pruning, no TurboQuant/JANGTQ anywhere in this bundle.

Setting	Value
Method	`jang-importance`
Target bits	2.0
Actual bits	3.12
Group size	64
Scheme	asymmetric affine
Backend	`mx.quantize`
Quantization objective	MSE
Scoring	weight-magnitude
Calibration	weights (no activation calibration)
MLP asymmetry floor	enabled
Hadamard rotation	disabled
Bit widths used	2, 3, 4, 6, 8
Passthrough (unquantized) tensors	0
Manifest	929 entries, schema v1, in `jang_config.json`

Notable per-tensor assignments:

Tensor class	Bits
`lm_head`	8
`embed_tokens`	6
dense-layer MLP (layers 0–2)	8
routed experts (`switch_mlp.{gate,up,down}_proj`)	2 (bulk of the model)

The 3.12 actual-bits figure against a 2.0 target is why this landed at 318 GB rather than the ~300 GB the campaign was aiming for — the asymmetry floor and the 8-bit high-precision slice cost more than budgeted.

Measured performance

All numbers below are from the unreleased mlxpress Python offload prototype on an M5 Max MacBook, 128 GB, experts streamed from internal NVMe, 45 GB hot cache, sampled (temp 0.7 / top-p 0.95 / rep-pen 1.1). These are the only numbers that exist for this bundle.

Routing config	Experts kept / 8	tok/s	Coherent
exact (all 8)	8.0	0.34–0.40	✅
min-keep 6 by router weight	6.2	0.67	✅
min-keep 5 by router weight	5.4	1.25	✅
min-keep 4 by router weight	4.6	1.85	✅
min-keep 4 + multithreaded reads	4.6	~2.8 (best stable)	✅
pure force-resident (drop by residency)	2.5	6.9	❌ garbage

No standard benchmarks (MMLU, HumanEval, GSM8K) were run on this bundle. The campaign was paused before evaluation. Quality was assessed only by manual inspection of generated text, which was coherent at every ✅ row above.

Two findings from those runs that may be useful to others:

Coherence needs only the ~4–5 highest-router-weight experts per layer, not all 8. Dropping the low-weight tail stays coherent down to 4.6/8. Dropping by cache residency instead of by router weight discards high-weight experts and immediately produces garbage.
Shrink-to-fit is not available for this model. Expert weight matrices were measured to be effectively full-rank and mutually orthogonal, so there is no low-rank or shared-basis trick that makes 40 B active parameters fit the ~50 GB of spendable cache on a 128 GB machine.

Known issues

lm_head is mislabeled. The three lm_head tensors are stored under the language_model. prefix (language_model.lm_head.{weight,scales,biases}) while all 3,300 other tensors use model.. This is a converter bug. Any loader must remap it — the offload prototype had to dequantize it and assign it as a plain fp16 tensor to get coherent output. Loading this bundle without handling that produces pure garbage, which is exactly how the bug was found.
MTP layer 78 is included. model.layers.78.eh_proj and its own MoE block are present. Any loader must either use or explicitly strip them. MTP was evaluated for the offload path and ruled out: verifying K speculative tokens touches K× distinct experts, so drive-bound cost scales with distinct experts read, not with forward passes.
318 GB does not fit in RAM on any shipping Apple Silicon machine. SSD offload is mandatory, and no released runtime implements it.
Not evaluated. See the benchmarks note above.

Files

File	Purpose
`model-*-of-00233.safetensors`	233 weight shards
`model.safetensors.index.json`	shard map, 3,303 tensors
`config.json`	architecture + MLX `quantization` block (per-tensor overrides)
`jang_config.json`	full JANG quantization manifest (929 entries)
`generation_config.json`	sampling + stop tokens
`tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`	tokenizer

License

MIT, inherited from zai-org/GLM-5.2.

Credit

Quantization, offload research, and this bundle by Jinho Jang — eric@jangq.ai. Base model by Z.ai (Zhipu AI).

Downloads last month: -

Safetensors

Model size

97B params

Tensor type

U32

F16

MLX

Hardware compatibility

Quantized

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