---
title: Hy4-preview-GGUF
canonical_url: "https://www.modelscope.ai/models/AngelSlim/Hy4-preview-GGUF"
md_url: "https://www.modelscope.ai/models/AngelSlim/Hy4-preview-GGUF.md"
repository: AngelSlim/Hy4-preview-GGUF
last_updated: 2026-09-03
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 70
stars: 0
---

# Hy4-preview-GGUF

> Hy4-preview-GGUF - An open-source model by AngelSlim on ModelScope. Three GGUF builds of Hy4-Preview: https://huggingface.co/tencent/Hy4-preview

- **Repository**: AngelSlim/Hy4-preview-GGUF
- **Downloads**: 70
- **Stars**: 0
- **Last updated**: 2026-09-03

Source: https://www.modelscope.ai/models/AngelSlim/Hy4-preview-GGUF

---

# Hy4-preview GGUF


Three GGUF builds of Hy4-Preview: https://huggingface.co/tencent/Hy4-preview

**Language / 语言:** [English](#english) · [中文](#中文)

| file | size | bpw | notes |
|---|---:|---:|---|
| `Hy4-preview-Q4_K_M.gguf` | 435.20 GiB | 4.86 | standard 4-bit, safe default |
|`Hy4-preview-UD-IQ1_M.gguf`| 219.83 GiB| 2.44 | Using the UD-IQ1_M quantization strategy, half the size |
| `Hy4-preview-STQ1_0.gguf` | 213.66 GiB | 2.38 | Using the MIX_STQ1_0 quantization strategy, half the size |

**Neither file runs on stock llama.cpp.** The `hyv4` architecture is not upstream. Apply the
patches in `hy4-preview-patch/`

---

<a name="english"></a>
## English

### 1. What these are

**`Hy4-preview-Q4_K_M.gguf`** — a conventional Q4_K_M. Most tensors are Q4_K; `ffn_down_exps`
gets Q6_K on 37 layers via llama.cpp's own logic. Use this unless you are memory-constrained.

**`Hy4-preview-UD-IQ1_M.gguf`** -  mixed precision with UD-IQ1_M strategy at ~2.44 bpw, roughly **half the size** for the
same model. The routed-expert `gate`/`up` projections run at 1.75 bpw (IQ1_M) and 2.0625 bpw (IQ2_XXS). 

**`Hy4-preview-STQ1_0.gguf`** — mixed precision with MIX-STQ1_0 strategy at ~2.38 bpw, roughly **half the size** for the
same model. The routed-expert `gate`/`up` projections run at 1.3125 bpw (STQ1_0) on 29 layers and
2.0625 bpw (IQ2_XXS) on the other 48. See section 3.


### 2. Running them

Build a patched llama.cpp 


```bash
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
git checkout 0cea36222

git apply hy4-preview-patch/0001-hyv4-architecture.patch
git apply hy4-preview-patch/0002-stq1_0-quant-and-cuda.patch   # skip if only using Q4_K_M

export PATH=/usr/local/cuda-13.0/bin:$PATH CUDACXX=/usr/local/cuda-13.0/bin/nvcc
cmake -B build-cuda -DGGML_CUDA=ON -DLLAMA_CURL=OFF -DGGML_NATIVE=OFF \
      -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 \
      -DLLAMA_BUILD_UI=OFF -DLLAMA_USE_PREBUILT_UI=OFF
cmake --build build-cuda --target llama-cli llama-bench llama-quantize -j 48
```

Set `-DCMAKE_CUDA_ARCHITECTURES` for your GPU (90 = H20/H100). Both `-DLLAMA_BUILD_UI=OFF` and
`-DLLAMA_USE_PREBUILT_UI=OFF` are needed for an offline build; the first alone still downloads
prebuilt assets.

Then

```bash
# single prompt
build-cuda/bin/llama-cli -m Hy4-preview-Q4_K_M.gguf -ngl 99 -c 8192 \
    --temp 0 -n 512 --no-warmup --jinja -st -f prompt.txt

# throughput
build-cuda/bin/llama-bench -m Hy4-preview-STQ1_0.gguf -ngl 99 -p 512 -n 128 -r 3
```

- **`--jinja` is required for chat.** The HY4 chat template matches no llama.cpp built-in family.
- **Keep the GGUF on local disk.** llama.cpp mmaps weights; over NFS random page faults run at
  ~12 MB/s, turning a 1-minute load into hours.
- **Use `-st -f prompt.txt` for a single prompt.** `-no-cnv` is ignored in this build and it will
  spin printing `>` on EOF.
- VRAM for full residency: ~435 GiB (Q4_K_M) or ~214 GiB (STQ1_0). With less, lower `-ngl`.

Measured on 8x H20:

| | prefill (pp512) | decode (tg128) |
|---|---:|---:|
| STQ1_0 | 204.56 ± 1.42 t/s | 20.47 ± 0.02 t/s |



Python tools reading these files must use the patched `gguf-py` with an **absolute** path:
`sys.path.insert(0, '/path/to/llama.cpp/gguf-py')`. 
### 3. STQ1_0 and the mixed-precision strategy

**The format.** STQ1_0 comes from llama.cpp PR #22836. Weights are ternary `{-d, 0, +d}`, with
**exactly one of every four lanes forced to zero** (3:4 sparsity). Each group of 4 weights is a
4-bit code plus a 1-bit table-select, indexing a 32-entry codebook; one fp16 scale covers 256
weights. That is `2 + 32 + 8 = 42` bytes per 256 weights = **1.3125 bpw**.

**Our encoder.** Upstream's quantizer targets QAT inputs already on the ternary grid: it ignores
the imatrix, sets `d = amax`, and zeroes `argmin |x|`. That is weak for post-training
quantization. We keep the format byte-identical and change only two decisions:

1. **Weighted least-squares scale**, `d = sum(w*sel*x) / sum(w*sel^2)` instead of `d = amax`.
2. **Imatrix-aware zero placement** — zero the lane minimising `w[j]*(x[j]^2 - (|x[j]|-d)^2)`,
   the *incremental* cost rather than the smallest magnitude.

alternating for 3 rounds. Measured on 1200 real expert rows: the LS scale alone gives **-89.7%**
weighted SSD, and the imatrix terms a further **-4.1%** of the remainder. The headline win is the
scale — `amax` pins `d` to the single largest outlier among 256 weights.

**Where the bits go.** The three routed-expert families are 97.7% of all parameters, so the
recipe spends freely on everything else:

| family | STQ1_0 build | why |
|---|---|---|
| `ffn_gate_exps` / `ffn_up_exps` | STQ1_0 (29 layers) / IQ2_XXS (48 layers) | the bulk; layer choice is imatrix-derived |
| `ffn_down_exps` | IQ3_XXS, IQ4_XS on last 3 | **writes straight into the residual stream**, so its error is not attenuated by a later gate — deliberately 2 levels higher |
| attention out / gate / q_a | Q5_K | llama.cpp only auto-bumps these when `n_expert == 8`; HY4 has 256 |
| MLA `q_b`/`k_b`/`v_b`/`kv_a_mqa` | Q8_0 | HY4's *split* names miss llama.cpp's substring match, so they get no automatic bump |
| DSA indexer | Q8_0 / F32 | 105 tensors, 0.21 GiB total, gates which 2048 tokens each query sees |
| iHC `*_fn`, router, norms, sink | F32 | mirrors the reference's `_keep_in_fp32_modules` |
| `output` (lm_head) | F32 | via `--leave-output-tensor` |

### 4. Building a runtime

#### Re-quantizing from bf16

The recipe files are included. **An imatrix is mandatory for STQ1_0** — its encoder uses it for
the scale solve and zero placement.

```bash
build-cuda/bin/llama-quantize --dry-run --imatrix imatrix.gguf \
    --tensor-type-file Hy4-preview-STQ1_0.tensortypes --leave-output-tensor \
    HY4.bf16.gguf out.gguf IQ1_M          # Q4_K_M build: use Q4_K_M as the base ftype
```

---

<a name="中文"></a>
## 中文

| file | size | bpw | notes |
|---|---:|---:|---|
| `Hy4-preview-Q4_K_M.gguf` | 435.20 GiB | 4.86 | 标准4bit gguf |
|`Hy4-preview-UD-IQ1_M.gguf`| 219.83 GiB| 2.44 | 使用 UD-IQ1_M 混合精度量化压缩 |
| `Hy4-preview-STQ1_0.gguf` | 213.66 GiB | 2.38 | 使用 MIX-STQ1_0 混合精度量化压缩 |


### 1. 这是什么

**`Hy4-preview-Q4_K_M.gguf`** —— 常规 Q4_K_M。多数张量为 Q4_K，`ffn_down_exps` 由 llama.cpp
自身逻辑提到 Q6_K（37 层）。**没有显存压力就用这个。**
**`Hy4-preview-STQ1_0.gguf`** —— 约 2.44  bpw 的混合精度，使用 UD-IQ1_M 混合精度量化压缩, 同一个模型**体积减半**。
**`Hy4-preview-STQ1_0.gguf`** —— 约 2.38 bpw 的混合精度，使用 MIX-STQ1_0 混合精度量化压缩, 同一个模型**体积减半**。路由专家的
`gate`/`up` 在 29 层用 1.3125 bpw（STQ1_0），另 48 层用 2.0625 bpw（IQ2_XXS）。见第 3 节。


### 2. 如何使用


```bash
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
git checkout 0cea36222

git apply hy4-preview-patch/0001-hyv4-architecture.patch
git apply hy4-preview-patch/0002-stq1_0-quant-and-cuda.patch   # 只用 Q4_K_M 可跳过

export PATH=/usr/local/cuda-13.0/bin:$PATH CUDACXX=/usr/local/cuda-13.0/bin/nvcc
cmake -B build-cuda -DGGML_CUDA=ON -DLLAMA_CURL=OFF -DGGML_NATIVE=OFF \
      -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 \
      -DLLAMA_BUILD_UI=OFF -DLLAMA_USE_PREBUILT_UI=OFF
cmake --build build-cuda --target llama-cli llama-bench llama-quantize -j 48
```

`-DCMAKE_CUDA_ARCHITECTURES` 按自己的 GPU 设置（90 = H20/H100）。离线构建**同时**需要
`-DLLAMA_BUILD_UI=OFF` 与 `-DLLAMA_USE_PREBUILT_UI=OFF`，只给前者仍会去下载预构建资源。


```bash
# 单条 prompt
build-cuda/bin/llama-cli -m Hy4-preview-Q4_K_M.gguf -ngl 99 -c 8192 \
    --temp 0 -n 512 --no-warmup --jinja -st -f prompt.txt

# 测速
build-cuda/bin/llama-bench -m Hy4-preview-STQ1_0.gguf -ngl 99 -p 512 -n 128 -r 3
```

- **chat 必须加 `--jinja`。** HY4 的 chat template 不匹配 llama.cpp 任何内置模板家族。
- **GGUF 必须放本地盘。** llama.cpp 用 mmap，NFS 随机页错误约 12 MB/s，本来 1 分钟的加载会变
  成几小时。
- **单条 prompt 用 `-st -f prompt.txt`。** 本 build 忽略 `-no-cnv`，遇 EOF 会一直打印 `>`。
- 全量驻留显存需求：约 435 GiB（Q4_K_M）或约 214 GiB（STQ1_0）。不够就降低 `-ngl`。

在 8 x H20 上实测（已确认 GPU 空闲、权重全驻显存）：

| | 预填充 (pp512) | 解码 (tg128) |
|---|---:|---:|
| STQ1_0 | 204.56 ± 1.42 t/s | 19.52 ± 0.01 t/s |


读这些文件的 Python 工具必须用打过补丁的 `gguf-py`，且用**绝对路径**：
`sys.path.insert(0, '/path/to/llama.cpp/gguf-py')`。

### 3. STQ1_0 与混合精度策略

**格式。** STQ1_0 来自 llama.cpp PR #22836。权重为三值 `{-d, 0, +d}`，且**每 4 个 lane 强制
一个为零**（3:4 稀疏）。每 4 个权重存成 4-bit code 加 1-bit 选表位，索引一张 32 项码本；每
256 个权重共用一个 fp16 scale。即每 256 权重 `2 + 32 + 8 = 42` 字节 = **1.3125 bpw**。

**我们的编码器。** 上游的量化器面向已落在三值网格上的 QAT 输入：直接忽略 imatrix，取
`d = amax`，并把零放在 `argmin |x|`。这对训练后量化（PTQ）很弱。我们保持格式**逐字节一致**，
只改两个决策：

1. **加权最小二乘 scale**：`d = sum(w*sel*x) / sum(w*sel^2)`，取代 `d = amax`。
2. **imatrix-aware 零位置**：零掉使 `w[j]*(x[j]^2 - (|x[j]|-d)^2)` 最小的 lane，即比较**增量**
   代价，而非单纯的最小幅值。

两者交替 3 轮。在 1200 行真实专家权重上实测：仅最小二乘 scale 就带来 **-89.7%** 加权 SSD，
imatrix 项在残差上再补 **-4.1%**。**主要收益来自 scale**——`amax` 会把 `d` 钉在 256 个权重里
的单个最大离群值上。

**bit 花在哪。** 三个路由专家族占全部参数的 97.7%，所以配方在其余张量上舍得花：

| 张量族 | STQ1_0 产物 | 原因 |
|---|---|---|
| `ffn_gate_exps` / `ffn_up_exps` | STQ1_0（29 层）/ IQ2_XXS（48 层）| 体积主体；选层由 imatrix 推导 |
| `ffn_down_exps` | IQ3_XXS，最后 3 层 IQ4_XS | **直接写回残差流**，误差不会被后续 gate 衰减，故刻意高两档 |
| attention out / gate / q_a | Q5_K | llama.cpp 只在 `n_expert == 8` 时自动提档，而 HY4 有 256 个专家 |
| MLA `q_b`/`k_b`/`v_b`/`kv_a_mqa` | Q8_0 | HY4 的**拆分**命名匹配不上 llama.cpp 的子串规则，完全拿不到自动提档 |
| DSA indexer | Q8_0 / F32 | 105 个张量共 0.21 GiB，却是决定每个 query 能看到哪 2048 个 token 的闸门 |
| iHC `*_fn`、router、norms、sink | F32 | 对齐参考实现的 `_keep_in_fp32_modules` |
| `output`（lm_head）| F32 | 通过 `--leave-output-tensor` |



#### 从 bf16 重新量化

配方文件已随附。**STQ1_0 强制需要 imatrix**——它的编码器要用 imatrix 做 scale 求解与零位置选择。

```bash
build-cuda/bin/llama-quantize --dry-run --imatrix imatrix.gguf \
    --tensor-type-file Hy4-preview-STQ1_0.tensortypes --leave-output-tensor \
    HY4.bf16.gguf out.gguf IQ1_M          # Q4_K_M 产物：基础 ftype 用 Q4_K_M
```
---

## Files

```
hy4-preview-patch/
  0001-hyv4-architecture.patch        18 files, +1632/-3   both GGUFs need this
  0002-stq1_0-quant-and-cuda.patch    25 files, +683/-4    STQ1_0 only
  Hy4-preview-STQ1_0.tensortypes      the STQ1_0 recipe
  Hy4-preview-Q4_K_M.tensortypes      the Q4_K_M recipe
```