Holo3 35B-A3B — JANGTQ2 (MLX)
TurboQuant codebook quantization of H Company's Holo3 GUI-agent VLM — routed experts at 2-bit via Lloyd-Max codebooks + Hadamard rotation, attention / embed / shared-expert / lm_head at 8-bit affine, vision tower preserved.
Model Details
| Property | Value |
|---|
| Base model | Hcompany/Holo3-35B-A3B (finetune of Qwen/Qwen3.5-35B-A3B) |
| Parameters (source) | 35 B total, ~3 B active per token |
| Architecture | qwen3_5_moe — 40 decoder layers: 30 Gated DeltaNet (linear attn) + 10 full attention, 256 routed experts + 1 always-on shared expert |
| Quantization format | weight_format: mxtq — routed experts via TurboQuant codebook (2-bit), everything else affine 8-bit or fp16 passthrough |
| Routed-expert storage | .tq_packed (uint32) + .tq_norms (fp16) + .tq_bits (uint8); codebook + Hadamard signs re-derived deterministically at load |
| Package size on disk | 11.63 GB across 12 shards |
| Shipped tensors | 1,930 total (1,597 language-model + 333 vision tower + 120 routed-expert TQ triples) |
| Vocab | 248,320 |
| Context (position embeddings) | 262,144 native |
| Vision tower | 27-layer ViT (hidden 1152, patch 16), preserved in fp16 |
| Chat format | Qwen im_start/im_end with <think> reasoning toggle; Holo3 XML tool-call grammar |
| Use case | GUI / computer-use agent (desktop, web, mobile) — designed for screenshot → action loops |
Quantization details, per tensor category
| Category | Bits | Group / codebook | Notes |
|---|
Routed-expert MLP (mlp.switch_mlp.gate_proj, up_proj, down_proj) | 2 (JANGTQ) | 2² Lloyd-Max centroids + Hadamard rotation | .tq_packed + .tq_norms + .tq_bits triples |
Embedding (embed_tokens), lm_head | 8 (affine) | group 64 | MLX-native QuantizedLinear |
Full-attention projections (q_proj, k_proj, v_proj, o_proj) | 8 (affine) | group 64 | Gate-doubled q_proj for attn_output_gate |
Linear-attention projections (in_proj_qkv, in_proj_z, in_proj_b, in_proj_a, out_proj) | 8 (affine) | group 64 | Gated DeltaNet |
Shared-expert MLP (gate_proj, up_proj, down_proj) | 8 (affine) | group 64 | Always active per token |
Router (mlp.gate) | fp16 passthrough | — | Precision-critical |
Shared-expert gate (shared_expert_gate) | fp16 passthrough | — | sigmoid scalar gate |
Norms (*_layernorm, *_norm), A_log, dt_bias, conv1d | fp16 passthrough | — | Un-quantized |
| Vision tower (333 tensors) | fp16 passthrough | — | patch_embed.proj axes pre-transposed to MLX layout |
JANGTQ ("TurboQuant") stores routed-expert weights as indices into a small Lloyd-Max codebook with a per-row norm, after a randomized Hadamard rotation that concentrates the distribution so quantization error is uniform. At inference, the input is rotated once per layer (cheap fused Metal kernel) and dot products happen against the codebook centroids directly, so we never dequantize back to affine. Compared to affine 2-bit at the same bit budget, this gives better quality and faster decode on the routed-expert MLP path.
Usage
JANGTQ requires our custom loader — stock
mlx_lm.load() can't parse
.tq_packed tensors. You need
jang-tools (free, public):
https://github.com/jjang-ai/jangq.
1pip install mlx mlx-lm mlx-vlm
2git clone https://github.com/jjang-ai/jangq && pip install -e ./jangq/jang-tools
Text
1from jang_tools.load_jangtq import load_jangtq_model
2from mlx_lm import generate
3
4model, tokenizer = load_jangtq_model("JANGQ-AI/Holo3-35B-A3B-JANGTQ2")
5print(generate(model, tokenizer,
6 prompt="The capital of France is",
7 max_tokens=64))
Image (VLM) — the intended use
Holo3 is a GUI agent: give it a screenshot and it localizes UI elements and plans actions.
1from jang_tools.load_jangtq_vlm import load_jangtq_vlm_model
2from mlx_vlm import generate
3from mlx_vlm.prompt_utils import apply_chat_template
4from mlx_vlm.utils import load_config
5
6path = "JANGQ-AI/Holo3-35B-A3B-JANGTQ2"
7model, processor = load_jangtq_vlm_model(path)
8config = load_config(path)
9
10prompt = apply_chat_template(
11 processor, config,
12 "Look at this desktop screenshot. Where should I click to open settings?",
13 num_images=1,
14)
15print(generate(model, processor, prompt, image="path/to/screenshot.png",
16 max_tokens=256))
Reasoning toggle
1msgs = [{"role": "user", "content": "What is 17 × 23?"}]
2# Reasoning OFF — pre-closed <think></think> block
3prompt = tokenizer.apply_chat_template(msgs, add_generation_prompt=True,
4 enable_thinking=False)
5# Reasoning ON — model fills the <think> block
6prompt = tokenizer.apply_chat_template(msgs, add_generation_prompt=True,
7 enable_thinking=True)
Pass enable_thinking as a direct kwarg (the chat_template_kwargs={...} form only propagates on some tokenizer versions).
Tool calls — Holo3 XML format
Holo3 emits tool calls in a custom XML grammar (not JSON). Pass tools=[...] to the tokenizer's chat template; the model responds in this shape:
1<tool_call>
2<function=click>
3<parameter=x>
4512
5</parameter>
6<parameter=y>
7384
8</parameter>
9</function>
10</tool_call>
Parse with a simple XML splitter on
<tool_call>. See H Company's
quickstart for a full agent harness example.
Video
The base model supports video via transformers and the bundle preserves video_preprocessor_config.json. mlx-vlm 0.4.4's prepare_inputs has no video path yet for qwen3_5_moe — for video, use upstream transformers.
Hardware notes
11.63 GB on disk; expect ~12–14 GB resident after load, plus KV cache.
| Mac unified RAM | Works? | Notes |
|---|
| 16 GB | ✅ text-only | Image inference will be tight at long context |
| 24 GB | ✅ comfortable | 32 k+ context, image inference OK |
| 32 GB | ✅ | 100 k context viable, comfortable VL |
| 64 GB+ | ✅ headroom | 262 k native context |
Upstream benchmarks
These are the base-model numbers for Hcompany/Holo3-35B-A3B, not evaluations of this JANGTQ2 quant:
| Benchmark | Score |
|---|
| OSWorld-Verified (computer use) | 77.8 % — SOTA at 3 B active |
| WebArena (web navigation) | State-of-the-art (see upstream card) |
| ScreenSpot-Pro (UI localization) | Top-tier (see upstream card) |
| OSWorld-G (visual grounding) | Top-tier (see upstream card) |
| H Corporate Benchmark (486 enterprise tasks) | Outperforms larger competitors |
Independent JANGTQ-quant evaluation is tracked in the jang-tools repo and will land in future README revisions.
Citation
1@misc{hai2025holo3modelfamily,
2 title = {Holo3 - Open Foundation Models for Navigation and Computer Use Agents},
3 author = {H Company},
4 year = {2026},
5 url = {https://huggingface.co/Hcompany/Holo3-35B-A3B}
6}
License
Apache 2.0 — inherits from the base model.
Packaged on Apple Silicon with
jang-tools (mlx-lm 0.31.2) by Jinho Jang (
eric@jangq.ai).
© 2026 Osaurus AI —
osaurus.ai
