---
license: other
license_name: modified-mit
license_link: https://huggingface.co/moonshotai/Kimi-K2.5/blob/main/LICENSE
library_name: transformers
pipeline_tag: image-text-to-text
base_model: moonshotai/Kimi-K2.5
base_model_relation: quantized
tags:
  - gsq
  - gumbel-softmax
  - quantization
  - ptq
  - moe
  - kimi
  - vllm
  - humming
  - arxiv:2604.18556
---

# Kimi-K2.5 — 2-bit GSQ

2-bit quantization of [`moonshotai/Kimi-K2.5`](https://huggingface.co/moonshotai/Kimi-K2.5)
(MoE, 384 experts, ≈260 GB FP) produced with **GSQ**
(Gumbel-Softmax Quantization). The model is compressed from ≈4.5 bpp down to
**≈2.13 bpp** while preserving most of the base model's reasoning, coding,
and long-context behaviour — and slightly *exceeds* the FP base on MATH 500
and LiveCodeBench v6 under our evaluation pipeline.

- Paper: [GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling](https://arxiv.org/abs/2604.18556) (arXiv:2604.18556)
- Paper page on HF: <https://huggingface.co/papers/2604.18556>
- Code: <https://github.com/IST-DASLab/GSQ>
- Collection: <https://huggingface.co/collections/ISTA-DASLab/gsq>

## Quantization details

- **Base model:** [`moonshotai/Kimi-K2.5`](https://huggingface.co/moonshotai/Kimi-K2.5)
- **Bits / weight (effective):** ≈2.13 bpp
- **Codebook:** 2-bit symmetric scalar `{-2, -1, 0, +1} × scale`
- **Group size:** 128
- **Format:** [Humming](https://github.com/inclusionAI/humming) (`quant_method: "humming"`, `b_dtype: "uint2"`)
- **Pipeline:** GPTQ initialization → Gumbel-Softmax refinement (Lion optimizer)
- **What's quantized:** routed-expert MLPs from layer 1 onward (`gate_proj`, `up_proj`, `down_proj`). Attention (`self_attn`), layernorms, embeddings, LM head, vision tower, MM projector, MoE routing `gate`, shared experts, and the first dense MLP layer (`layers.0.mlp.*`) are kept in BF16.

### Storage layout (why the HF UI shows I32 + BF16)

The Hugging Face "Tensor types" widget reports the **container dtype** of each
safetensor on disk, not the effective precision of the underlying weights.
This checkpoint uses the **Humming** on-disk layout (exact-width packing — no
sub-byte values are padded into a wider container). For every quantized
expert-MLP `Linear` with original weight shape `[out_features, in_features]`,
the following tensors are stored:

| Tensor                                  | Dtype | Shape on disk                       | Meaning                                                                       |
|-----------------------------------------|-------|-------------------------------------|-------------------------------------------------------------------------------|
| `<layer>.weight`                        | I32   | `[out_features, in_features × 2 / 32]` = `[out_features, in_features / 16]` | 2-bit values bit-packed along the input dim, LSB-first: 16 weights per INT32 word. |
| `<layer>.weight_scale`                  | BF16  | `[out_features, in_features / 128]` | One symmetric scale per group of `group_size = 128` weights along the input dim. |
| Attention / norms / embed / LM-head / vision / MM-projector / MoE `gate` / shared experts / `layers.0.mlp.*` | BF16 | unchanged | Not quantized; copied from the base checkpoint. |

So although the UI says "I32 + BF16", the **effective storage** per quantized
weight is `2 bits (packed) + 16 bits / 128 (group scale) ≈ 2.13 bpp`. The
`quantization_config` block in `config.json` is:

```json
{
  "quant_method": "humming",
  "b_dtype": "uint2",
  "weight_scale_group_size": 128,
  "weight_scale_type": "group",
  "has_zero_point": false,
  "ignore": [
    "lm_head",
    "re:.*embed_tokens.*",
    "re:.*self_attn.*",
    "re:.*input_layernorm.*",
    "re:.*post_attention_layernorm.*",
    "re:.*\\.norm$",
    "re:.*vision_tower.*",
    "re:.*mm_projector.*",
    "re:.*mlp\\.gate$",
    "re:.*shared_expert.*",
    "re:.*layers\\.(0)\\.mlp\\.(gate_proj|up_proj|down_proj|gate_up_proj).*"
  ]
}
```

Loading this checkpoint requires vLLM plus the
[`humming`](https://github.com/inclusionAI/humming) MoE kernels (`pip install
humming-kernels`). See **Serving with vLLM** below.

> Note: GSQ training first writes shards in `compressed-tensors`
> `pack-quantized` format (where the 2-bit codebook is padded into a 4-bit
> INT32 container). The published checkpoint here has been re-packed via
> `convert_to_humming.py` into exact-width 2-bit Humming storage, hence the
> `2 / 32` shape factor on `weight`.

## Serving with vLLM

Install the Humming kernels (required for vLLM to load this checkpoint):

```bash
pip install humming-kernels
```

Hopper (sm_90) or Ampere (sm ≥ 80) GPUs required for serving. On 8× H100/H200,
valid TP sizes are `1, 2, 4, 8` (Marlin MoE constraint with group size 128).

```bash
vllm serve ISTA-DASLab/Kimi-K2.5-2Bit-GSQ \
  --tensor-parallel-size 8 \
  --trust-remote-code
```

## Citation

```bibtex
@article{gsq2026,
  title  = {GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling},
  author = {Dadgarnia, Alireza and Tabesh, Soroush and Nikdan, Mahdi and Helcig, Michael and Kurti{\'c}, Eldar and Kleinegger, Max and Alistarh, Dan},
  journal= {arXiv preprint arXiv:2604.18556},
  year   = {2026},
  url    = {https://arxiv.org/abs/2604.18556}
}
```